Biology & Medicine

From the Bitter Lesson of AI scaling to the unsolved mysteries of protein folding , Google DeepMind’s Pushmeet Kohli and Biohub’s Sal Candido are rethinking what it takes to build AI that truly understands biology. In this special panel moderated by Brandon Anderson , they explore why AlphaFold’s breakthrough was only the beginning , why scaling compute and data alone won’t solve biology, and how the next generation

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How I used AI to investigate millions of historical records and surfaced a forgotten meteorite report, three lost rhinos, and unrecorded volcano eruptions. Last week, on October 1st, 2026, the historian Benjamin Breen published a post called Using Opus 5.5 to discover a new eyewitness account of the dodo. He used AI to search digitized records from the Dutch East India Company. The Company dominated the spice trade a

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Nature Computational Science, Published online: 09 October 2026; doi:10.1038/s43588-026-01063-0 Researchers pair active learning with geometric deep learning to predict the outcome and site selectivity of chemical reactions from scarce data, guiding experiments that accelerate drug discovery and expand accessible chemical space.

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Why is Anthropic running a biology lab? Anthropic is best known for Claude, the artificial intelligence chatbot that competes with OpenAI’s ChatGPT and Google’s Gemini. But the AI company now employs molecular biologists, studies DNA, and operates a laboratory where scientists conduct physical experiments. The San Francisco Bay Area facility is what researchers call a “wet lab”: a laboratory where scientists work dir

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image credit- shutterstock China's XtalPi Holdings, an AI and robotics-driven drug and materials discovery company, has debuted Kodexia™, the industry's first closed-loop small interfering RNA (siRNA) discovery platform combining generative AI, first-principles-driven biological modeling, and automated experimentation. Validating the rapid execution capabilities of this new infrastructure, the company simultaneously

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Drug discovery for neurological disease has traditionally centered on the molecules altered by disease. But the molecules that cause pathology are not necessarily the best points from which to reverse it. Here, we ask which otherwise unaltered molecular control points can be engaged to restore pathological neural circuits toward functional states. We present CircuitATLAS, a provenance-grounded systems-neuroscience kn

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Invasive candidiasis has become more common in recent decades, particularly within ICUs. Identifying specific Candida spp., and testing their sensitivity to antifungal drugs is crucial for effective treatment that helps healthcare providers to detect potential treatment challenges early. This study aimed to identify Candida spp. at their spp. level present in blood, assess their susceptibility to antifungal agents, a

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Alphabet (GOOGL) and Meta Platforms (META) are buying into AI biology. On Wednesday, October 7, Google DeepMind, Alphabet’s drug discovery unit Isomorphic Labs, and Meta agreed to put a combined $300 million into Biohub’s Virtual Biology Initiative. Biohub is the science group backed by Mark Zuckerberg and Priscilla Chan. With the U.S. Department of Energy and the National Institutes of Health also in, total commitme

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In a series of interviews, we’re meeting some of the PhD students that were selected to take part in the Doctoral Consortium at the International Joint Conference on Artificial Intelligence and the 29th European Conference on Artificial Intelligence (IJACI-ECAI 2026) . This time, we hear from Mayra Elwes about her work developing machine learning methods for clinical time-series data. Tell us a bit about your PhD – w

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SOUTH SAN FRANCISCO, Calif., October 06, 2026--insitro, the AI company unlocking causal human biology to systematically generate high-impact therapeutics, today announced that the advanced machine learning models that insitro built with Eli Lilly and Company (Lilly) to predict in vitro and, critically, in vivo properties of small molecules can now be used by certain biotech companies in Lilly TuneLab™. Trained on mul

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Share SOUTH SAN FRANCISCO, Calif. – October 6, 2026 – insitro, the AI company unlocking causal human biology to systematically generate high-impact therapeutics, today announced that the advanced machine learning models that insitro built with Eli Lilly and Company (Lilly) to predict in vitro and, critically, in vivo properties of small molecules can now be used by certain biotech companies in Lilly TuneLab™. Trained

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Share Last year, insitro and Eli Lilly and Company (Lilly) teamed up to build AI models that predict in vivo properties of small molecules, powered by the corpus of proprietary data that Lilly has accumulated over decades of R&D. Together, we built the in vivo PK model available in Lilly TuneLab as of October 6, to predict in vivo pharmacokinetics (PK) across four preclinical species: mouse, rat, dog, and monkey. Des

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As spatial molecular data grow in scope, there is a pressing need to identify disease-associated spatial structures. Current approaches typically make restrictive assumptions such as representing tissue regions by abundances of discrete cell types and samples by abundances of discrete niches; this risks overlooking important signals. Here we introduce variational inference-based microniche analysis (VIMA), a method c

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Large language models (LLMs) and vision-language models (VLMs) are usually frozen after deployment, so they do not learn from the cases they solve. This is especially concerning in medicine, where new clinical evidence, updated guidelines, and new therapies can change established practice. Fine-tuning can update the model, but it requires access to model weights and additional training. Parameter-free methods avoid t

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Large language models (LLMs) are increasingly relied upon to support ambient documentation and clinical reasoning. Here we examine the impact of a failure mode shared between these two applications by assessing their sensitivity to information incidental to the patient encounter. In 576 patient-clinician dialogues, we found that frontier models inserted small-talk exchanges into 35% of notes, while mean quality score

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Nature Methods, Published online: 06 October 2026; doi:10.1038/s41592-026-03242-3 Identification of disease-associated patterns in spatial molecular data is challenging. We introduce variational inference-based microniche analysis (VIMA), a deep learning-based statistical method that can identify such patterns without requiring annotation of the data into cell types or niches. VIMA has high power and fidelity across

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October 6, 2026 Arbaaz Muslim, Software Engineer, Google Research, and Gautam Prasad, Software Engineer, Google Research With Google Earth AI’s Population Dynamics Foundation Model (PDFM), we can address the data gaps and temporal reporting lags of existing epidemiological workflows. In our latest work, we present five partner-driven case studies demonstrating how this model exemplifies the planetary geospatial found

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Site-specific fertilizer recommendation systems adapt nutrient advice to location, soil properties, crop type, and production targets, but scientific reuse is constrained when recommendation functions remain accessible mainly through interactive interfaces, outputs are not versioned, and trained approximations cannot be independently loaded or benchmarked. This technical report presents the Turba fertilizer machine l

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Programmable RNA sensors such as toehold switches are used to detect specific RNA sequences. However, their reliance on protein-based or RNA-based outputs limits their use in multiplexed and sequencing-based applications. Here we introduce Detectrons, modular biosensors that couple programmable toehold switches with retron-mediated reverse transcription to transduce RNA inputs into unique DNA barcodes, converting dyn

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Google DeepMind creates watermarked AI-designed proteins that still work in lab tests SynthID Bio embeds a detectable signature into AI-generated protein sequences and structures, with Nature research showing the watermark can survive without significantly disrupting biological function Google DeepMind researchers have demonstrated a way to watermark AI-designed proteins while preserving their biological function, an

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Google DeepMind watermarks AI-designed proteins, and the first binders still bind Google DeepMind has taken its SynthID watermark into biology, and says the first watermarked protein binders still bind. In wet-lab testing against three target proteins, VEGF-A, the SARS-CoV-2 spike protein RBD and PD-L1, the watermarked designs “matched the hit rate, binding affinity, and natural sequence diversity of unwatermarked ve

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Image 1. A matriarch leads her family across the dry lake bed in Amboseli, Kenya. Such a movement is typically preceded by single and chorused Let’s go and Cadenced rumbles as individuals initiate and negotiate a plan of action. Image Credit: ElephantVoices . Key Takeaways Around 60% of elephant rumbles occur in overlapping choruses , yet these multi-caller exchanges have been far less studied than isolated calls. In

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In what can be called a major breakthrough for synthetic biology and AI-powered biosecurity, Google DeepMind, Google’s AI lab, has developed a new method to watermark AI-designed proteins, establishing a permanent proof of origin directly inside biological code. Published in the journal Nature, the proof-of-concept technology, which is named SynthID Bio, solves a long-standing technical challenge: embedding an non-no

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Introducing SynthID Bio: Google DeepMind Puts Watermarks Inside AI-Designed Proteins Google DeepMind is introducing SynthID Bio after laboratory tests produced the first watermarked protein binders that retained their intended biological function. The September 30 announcement moves AI watermarking beyond images, text, audio, and video. Its signature can remain detectable in a synthesized physical protein, not merely

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October 1, 2026 report Google DeepMind develops invisible watermarks for AI-designed proteins Paul Arnold Author Gaby Clark Scientific Editor Robert Egan Senior Editor Watermarks have long protected everything from bank notes and fine art to digital photographs and software, helping prove authenticity and trace an object's origin. Their main function is to protect copyright and verify authenticity. That same traceabi

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Typed decision models answer schema-constrained questions about a text in one forward pass and return probabilities meant to be thresholded. We ask whether biomedical sentence encoders trained for retrieval are good starting points for such models. We present SBERT2S1, which converts Sentence-Transformers encoders into bi-encoder, cross-head (C) and prior-fused residual (PFR) decision models, together with BIODECIDE,

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Introducing SynthID Bio Proof of concept for watermarking AI-generated proteins while preserving biological function. Today, we’re introducing SynthID Bio to bring watermarking technology to synthetic biology. SynthID Bio embeds an imperceptible signature directly into the biological code, ensuring the watermark is verifiable not just on a digital model but on the synthesized, physical protein itself – all while pres

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Inflammation is widely considered a hallmark of animal immune responses. In mammals, this process is mediated in part by pro-inflammatory cytokines of the interleukin 1 (IL-1) superfamily, which establish and support microenvironments that regulate immune cell activity. However, the evolutionary origins of IL-1 cytokines remain elusive, as homologous proteins have been difficult to confidently identify outside verteb

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Antibody-based single-cell technologies, such as flow cytometry, mass cytometry and CITE-seq, have become widely used in clinical diagnostics and basic research; however, their analysis is complicated by technical noise, batch effects, platform differences and restricted antibody panels. Here we present CytoVI, a probabilistic generative model for statistically rigorous unified analysis of antibody-based single-cell

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Large language models rely heavily on human text, which often conveys surface answers rather than the spatial and structural logic behind them. Protein folding is a natural testbed, because one solved structure yields thousands of exactly checkable spatial and topological statements. We ask: can learning to fold proteins teach general models reusable reasoning capabilities? To answer this, we build FoldingCorpus, a p

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Scientific software presents a demanding test for computer-using agents based on visual language models (VLMs): completing a research workflow requires interpreting specialized interfaces, manipulating scientific objects, and producing verifiable results. We thus introduce OSWorld-Science, a benchmark and evaluation environment that combines scientifically meaningful tasks, artifact-based evaluation, and an efficient

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Biology doesn't operate in silos, and neither should the AI representation of it. Quine is an early-stage research effort to create a multimodal world model of biology. By connecting insights across biological scales and modalities, Quine helps scientists computationally search a space far larger than intuition allows and prioritize hypotheses before they reach the lab. Experimental results provide important feedback

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AI Models & Platforms Microsoft Research Debuts Quine, a Multimodal World Model of Biology Add Unite.AI to your preferred sources on Google Microsoft Research on September 29, 2026, introduced Quine, a research effort built around a multimodal world model of biology and an interactive harness that links the model to orchestration and reasoning models, scientific tools, the research literature, the wet lab, and the te

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Listen: Solving all disease is one of the most monumentally complex challenges in science. Today, for the first time, AI provides the foundation to make it achievable. Tackling the frontier of human health is our mission, and it means identifying exactly where modern AI can make the greatest difference. We see drug discovery as the critical wedge in solving all disease, and therefore our initial focal point where AI

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From left: Dr Liang Qiyu, research fellow at the Institute for Digital Molecular Analytics and Science at NTU, and Professor Miao Yansong. Credit: NTU Singapore. Scientists at Nanyang Technological University, Singapore (NTU Singapore) have developed two artificial intelligence (AI)-powered platforms that could enable researchers to more accurately predict proteins that undergo phase separation – a process through wh

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Multidisciplinary tumor boards integrate multimodal clinical observations and longitudinal patient histories through specialist discussions, yet benchmarks rarely capture these real-world trajectories. We introduce OpenTumorBoard, a benchmark with 611 patient cases and 19,157 discussion turns across ten specialist roles, transcribed from 12,534 minutes of publicly available tumor board recordings on YouTube. The benc

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Anthropic touts AI-led biology discovery Andrew Zinin Chief Editor U.S. artificial intelligence startup Anthropic on Wednesday unveiled the first discovery from its molecular biology lab, a previously unknown enzyme system spotted by its Claude model and touted by the company as proof that AI can speed up basic research. Enzyme systems found in nature have led to some of biology's biggest breakthroughs, from the tool

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Anthropic touts AI-led biology discovery - Anthropic's AI model Claude discovered a new enzyme system called ART by analysing DNA sequences, potentially revealing a new gene-editing mechanism similar to CRISPR. - The AI autonomously analysed over 200,000 genes in 21 hours, accelerating research that usually takes weeks or months. - The discovery, still preliminary and unreviewed, could lead to advances in gene therap

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Retrieval-based factuality evaluation, where LLM-generated claims are verified against evidence from authoritative medical corpora, has become the dominant paradigm for scalable hallucination detection in high-stakes clinical settings. Despite the urgency of reliable and transparent medical fact verification, most systems measure performance with aggregate metrics like F1, which obscure where and why failures occur.

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Drawing inspiration from the Millennium Prize Problems in mathematics, Edison Scientific and FutureHouse have assembled a set of Millennium Problems for Biology. Each problem is intended to be very difficult to solve, but easy to validate in a simple laboratory environment. For this reason, grand challenges like “Solve Aging” that are very important and challenging but very hard to validate are excluded. Solving any

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Science Sep 23, 2026 We’re introducing a new life sciences research group and laboratory at Anthropic. Our focus is on fundamental biology research using Claude: exploring datasets of DNA to identify uncharacterized protein families, generating hypotheses at scale, and testing them through experiments in the lab. This post introduces the team behind this work and shares early results in which Claude discovered a nove

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Radical Numerics co-founder Eric Nguyen discusses genomic language models and biosecurity. He helped develop Evo and Evo 2; a separate Arc/Stanford team later used those models to generate and synthesize functional bacteriophage genomes. The interview distinguishes three sequence scales: an average human gene is about 60K, long genes can reach 2.3M, and the whole human genome is around 3B. Nguyen argues that models w

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Nature Machine Intelligence, Published online: 23 September 2026; doi:10.1038/s42256-026-01304-x Computational drug design often generates molecules that cannot be synthesized. Li and Lai show how large language models can predict precise structural edits to fix these molecules, outperforming traditional methods and preserving key features.

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**media[1229066]**Viva-Thera makes its public debut with four modalities pursuing HIV prevention, treatment and cure, supported by XtalPi's platform capabilities.Viva-Thera combines its first atomic-resolution MPER-TMD-CT structure with XtalPi's AI and automation platform to advance a small-molecule PCC.MPER vaccine immunogens elicit antibody levels approximately five times those of the study comparator in rhesus mac

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This September 22 secondary report describes Anthropic’s Bay Area biology laboratory and reports plans for Claude-directed robotic experiments. In its September 23 announcement, Anthropic explicitly states that all lab work in the laboratory it describes is performed by human scientists. Claude searches genomic data, proposes candidate systems and assists analysis. The two sources differ in date and claim; the earlie

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Rapid Oral presentation will report initial Phase I data for ISM6331 in patients with advanced solid tumors FDA Fast Track- and Orphan Drug-designated program reflects Insilico’s strategy to advance AI-designed medicines against difficult-to-treat, age-associated diseases CAMBRIDGE, Mass. — September 22, 2026 — Insilico Medicine ("Insilico"; HKEX: 3696), a clinical-stage generative artificial intelligence (AI)-driven

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A collaborative system of large language model agents brings protein engineering closer to natural-language interaction, lowering the barrier between human intent and biomolecular design. This is a preview of subscription content, access via your institution Access options Access Nature and 54 other Nature Portfolio journals Get Nature+, our best-value online-access subscription 27,99 € / 30 days cancel any time Subs

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Alex Zhavoronkov, co-CEO of the AI drug discovery startup Insilico Medicine, dresses the same way at all his public engagements: a black padded vest with a small white badge that keeps count of the company’s clinical pipeline. When Fortune sat down with him in Hong Kong in July, he made sure to point out the badge’s latest revision: a small circle beside “Phase 3.” The badge is a provocation as much as a point of pri

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Claude made the open-source models that scientists use to predict and design biomolecules faster and more memory-efficient. Claude optimized more than 30 of these models in just under four weeks, speeding them up roughly 4x on average. It also created a low-memory mode that enables the accurate prediction of biomolecular systems larger than 10,000 tokens (amino acids, nucleotides, and atoms from small molecules and i

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AI Models & Platforms Insilico Medicine Releases Open Longevity AI Toolkit in Cell Study Add Unite.AI to your preferred sources on Google Insilico Medicine on September 17, 2026 announced the publication of a study in Cell introducing an openly released AI toolkit for aging biology: the LongevityBench benchmark, a family of five compact Longevity-LLMs, and the Longevity Claw agentic research platform. Publication in

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Artificial intelligence (AI) in radiology aspires to deliver expert-level diagnosis across diverse clinical tasks, yet existing supervised strategies remain limited in scope. We developed RADAR, a generalist vision-language model trained on more than 400,000 contrast-enhanced abdominal computed tomography (CT) examinations and 15 million anatomy-wise image-text pairs, learning directly from clinical reports without m

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Cell-state diversity drives tissue adaptability, repair, and disease resilience, but capturing this complexity is a challenge. Current approaches rely on transcriptional profiling and overlook organelle structure, a key indicator of metabolism and stress. We developed spatial Organellomics (sOrganellomics), an imaging workflow that integrates automated segmentation with machine learning to classify and spatially map

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Research published in the Journal of Medicinal Chemistry showcases a novel PROTAC series from Insilico Medicine UAE, advancing local research in targeted protein degradation - Insilico Medicine AI R&D Center in Abu Dhabi adds a new discovery milestone to the company’s UAE operations, with research on BTK degraders for potential applications in blood cancers. - Chemistry42 supported the design of novel molecular build

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The paper introduces three interconnected resources for the global scientific community: - LongevityBench, the first open benchmark designed to rigorously evaluate AI reasoning across multiple domains of aging biology; - Longevity-LLMs, a family of compact, open-source language models specifically trained on clinical and multi-omics aging data; and - Longevity Claw, an open-source agentic research platform that auton

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JEPA-Anything: Learning Predictive Models across Different Worlds 摘要 World modeling enables intelligence to anticipate consequences, guide interventions, and learn from interaction. Yet predictive models remain domain-specific: can a common learning principle support world modeling across radically different systems? We introduce JEPA-Anything, a domain-agnostic framework based on orthogonal predictive factorization

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Artificial intelligence (AI) is changing the way biomedical research is conducted by making it possible to integrate diverse types of biological data and tackle questions that were previously difficult to address. At the same time, its growing use also raises important questions about data quality, reliability, interpretability, and how computational predictions should be combined with biological knowledge and experi

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It’s a pharmaceutical-company executive’s dream: tens of thousands of employees working day and night, uninterrupted by meals, sleep or distraction, to find the next blockbuster drug. A sophisticated artificial-intelligence system called the Virtual Biotech, which is described today in the journal Science1, is a step towards this vision. The Virtual Biotech comprises as many as 37,000 agents — AI systems that autonom

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Understanding why people age — and how to slow that process down — is an obsession that has stretched across millennia, from mythic quests for immortality to modern science’s hunt for drugs that lengthen lifespan. Now, that quest has entered the AI era. A report1 published today in Cell unveils a bespoke artificial-intelligence system that aims to turbocharge longevity research. The system introduces large language m

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scBaseCount is presently the largest public single-cell RNA-seq repository, containing over 502 million cells across 27 organisms and 75 tissues. An AI agent autonomously discovers, annotates, and uniformly reprocesses all 10× Genomics datasets in the SRA, creating a harmonized, continually updated resource for studying the diversity of cell biology and training AI models.

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By Tom Cassauwers When asked why Jean-Olivier Irisson, a scientist at Sorbonne Université in Paris, decided to dedicate his life to studying microscopic creatures in the sea, his answer was simple: “They are beautiful.” Beauty may not be the first thing that comes to mind when we think of plankton – organisms that drift in water and come in an extraordinary variety of shapes and sizes. But images by Irisson’s team te

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The program applies generative AI and programmable RNA medicine to design single-administration, self-limiting in vivo cell therapies that clear the earliest culprit cells of age-related disease. - The new research initiative aims to develop programmable, self-limiting preventive treatment to eliminate early cellular drivers of age-related diseases. - Powered by Pharma.AI and aging foundation models, the approach tar

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Single-cell foundation models (scFMs) are transforming computational biology by enabling generalizable, task-agnostic representations for versatile single-cell analysis. Despite their progress in facilitating rapid deployment for downstream tasks, off-the-shelf scFMs still have some overlooked concerns: (I) (Pretraining Cost.) Pretrain-based scFMs necessitate pretraining on a vast volume of cells, rendering it draini

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Insilico Medicine announced the publication of a study in Nature Biotechnology showing that its AI-discovered idiopathic pulmonary fibrosis drug candidate rentosertib produced reductions in predicted biological age across six independently developed proteomic aging clocks. The research analyzed longitudinal serum proteomic data collected during a Phase IIa clinical trial of rentosertib in idiopathic pulmonary fibrosi

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Large language models (LLMs) can generate biomedical hypotheses, but it remains unclear whether they truly reason from scientific evidence or simply produce convincing-sounding ideas. To study this, we combine three major biological databases: the Kyoto Encyclopedia of Genes and Genomes (KEGG), Rhea, and UniProt, into a unified biochemical knowledge graph and construct a benchmark of 550 paths connecting enzyme sourc

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Human communication relies on more than just talking; we nod, shrug, move our hands and point with our fingers. For people with severe paralysis, brain–computer interfaces (BCIs) can convey intended speech or movement. Now, researchers have shown that a single brain implant can do both at once. The device, described1 today in Nature Neuroscience, is the first to translate both verbal and non-verbal modes of communica

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For drug discovery, protein-folding models such as AlphaFold have a data problem: there isn’t enough of it in public databases. Improving the performance of these artificial-intelligence-based tools will require extra data that provide examples of how proteins and drugs interact, some scientists argue. Protein structures— locked away by the thousands in drug company vaults— offer one promising source. Today, a consor

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Simultaneous characterization of proteins, RNAs and glycans with a nanopore remains a substantial challenge. To advance toward this goal, we introduce a multifunctional adaptor, maleimido-C2-formylphenylboronic acid (maleimido-C2-FPBA), into the Mycobacterium smegmatis porin A (MspA) nanopore at the pore constriction site. Here we demonstrate that the engineered nanopore, MspA-FPBA, enables the simultaneous identific

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Background: Histopathological diagnoses in kidney transplant biopsies, as described by the Banff classification, suffer from substantial inter-observer variability and misinterpretation, making them prone to diagnostic errors. Continuous indices based on BanffNET automated lesion scores are proposed to quantify the continuous phenotypic spectrum of kidney transplant biopsies, complementary to pathologist assessment o

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I often say that I think that antibody design is one of the better use cases for AI drug discovery at present, because you start with a lot of inherent advantages: antibodies as a class are evolutionarily optimized for long plasma half-lives, and the long list of approved antibody drugs shows you that success is out there to be had. But this is a relative scale! I still believe that antibodies are one of the more rea

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CAMBRIDGE, Mass., Sep 10, 2026 — Insilico Medicine (“Insilico,” HKEX: 3696), a clinical-stage biotechnology company powered by generative AI, today announced that it has dosed the first patient with Rentosertib (known as ISM001-055 / INS018_055) in GENESIS-IPF-3, a Phase III clinical trial (NCT07687459, CTR20262475) at Peking Union Medical College Hospital, while Shanghai Pulmonary Hospital has also enrolled its firs

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Artificial intelligence (AI) is increasingly being applied to the design of simple genetic systems, but its ability to generate whole functional genomes has remained untested. Writing in Science, King and colleagues report the generative design of complete bacteriophage genomes. The authors used the genomic language models Evo 1 and Evo 2, which were pretrained on large datasets of DNA sequences, including more than

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Nature Computational Science, Published online: 10 September 2026; doi:10.1038/s43588-026-01042-5 This Resource introduces PathSegmentor—a foundation model that segments 160 pathological categories via natural language prompts, generalizes across datasets and clinical cohorts, and enables scalable, interpretable pathology image analysis.

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Nature Computational Science, Published online: 10 September 2026; doi:10.1038/s43588-026-01049-y MutexaGPT turns plain-language enzyme-engineering intuitions into lead mutation designs through AI agent-orchestrated high-throughput molecular modeling, yielding experimentally validated improvements in enzyme specificity and cold activity.

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Nature Computational Science, Published online: 10 September 2026; doi:10.1038/s43588-026-01055-0 By integrating intracranial neural signals from gray and white matter, we developed personalized deep learning models that accurately decoded continuous valence and arousal states across image and video viewing tasks. The models generalized across tasks, revealed emotion-encoding brain subnetworks and enabled low-latency

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Google DeepMind has created an AI-powered map of about 9 billion possible single-letter changes in human DNA, giving scientists a new way to search for genetic mutations that could matter for disease and biology. Called AlphaGenome Atlas, the platform contains precomputed predictions for every possible single-nucleotide variant in the human genome. DeepMind says testing billions of possible changes individually in la

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AlphaGenome Atlas: A predictive map of every possible DNA letter change in the human genome How predicting the molecular impact of every possible single-letter DNA variant in the human genome will help accelerate our understanding of biology. Today, we are introducing AlphaGenome Atlas: a platform (detailed in our paper) containing predictions for the effects of 9 billion single-nucleotide variants — every single-let

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Google DeepMind said Tuesday that it has used artificial intelligence to predict the biological consequences of all 9 billion possible single-letter changes to human DNA, and is making the resulting database available free to academic researchers worldwide. AlphaGenome Atlas, as DeepMind calls the database, is a precomputed catalogue of what each substitution of a single DNA base is likely to do to the machinery that

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Physical equations in protein modeling appear to have been replaced by generative models trained directly on structure data. By learning a mapping from noise to data, sampling de novo protein structures has become much more efficient. However, such models can also learn non-physical features and break down with out-of-distribution settings which are typical in protein design campaigns. In this review, we highlight wh

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Context-specific genome-scale metabolic modeling begins with scoring which of the approximately 10,600 human reactions are active in a patient's tumor. Methods in this literature are routinely benchmarked against activity labels obtained by thresholding the same transcriptomic matrix that is supplied to the model as input. We report a self-audit of our own graph attention scorer, MetaGNN, evaluated on TCGA colorectal

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Across application domains, predictive signals often sit in unstructured free text rather than structured fields, yet turning that text into useful and interpretable features is difficult. Running large language models (LLMs) over an entire corpus is costly and hard to reproduce, while end-to-end text representations can rely on surface cues that are difficult to inspect. We present an agentic workflow that takes a p

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Background: Weighted Gene Co-expression Network Analysis (WGCNA) is a widely adopted systems biology method to discover gene modules and module-trait associations, mostly from transcriptomics. Designed for a single layer, it cannot jointly analyze multi-omics layers, a consequential limitation in modern biomedical research. WGCNA modules are often hard to interpret, requiring vast follow-up for contextualization. Mor

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Accurate, reliable, large-scale disease predictions are essential to ensure rice production. Existing disease prediction models often face a trade-off between interpretability and predictive capability, necessitating the integration of mechanistic knowledge and data-driven learning within a modelling framework. Accordingly, we propose a physics-informed hybrid gated recurrent unit (PI-HGRU) model for spatiotemporal d

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Why did a big clinical trial that tested an anti-inflammatory drug fail to reduce heart events? Why did another pivotal trial of lowering Lp(a) fail to reduce heart events? In this edition of Ground Truths I’ll offer some explanations and introduce some new and relevant information. In part, this is about relying on surrogate endpoints like increased blood levels of interleukin-6 or Lp(a), which have been generally r

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Digital twin technologies could transform neuroscience and biomedicine by creating predictive computational representations of living organisms. However, most animal digital twins model neural circuits, anatomy, or biomechanics separately rather than integrating the processes that generate behavior. We argue that animal digital twins should instead be conceived as embodied dynamical systems that unify neural activity

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Protein language models (PLMs) provide powerful representations of protein sequence, but their utility for proteome-scale binding-site retrieval remains unclear. Here, we present PocketScope, a training-free framework that represents cavity-lining residues using frozen ESM-C 600M embeddings and retrieves related binding sites through exhaustive late-interaction MaxSim, without pooling or approximate nearest-neighbor

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Objectives: Dentists prescribe approximately one in ten antibiotics worldwide, yet antimicrobial stewardship (AMS) remains underemphasized in dental education. Large language models (LLMs) may support AMS training, but their proficiency and clinical reasoning in this context remain unclear. We evaluated GPT-4o's accuracy and clinical reasoning on dental antibiotic prescribing questions, stratified by question difficu

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Accurate identification of drug-target interactions is consequential for novel drug discovery and development. Deep learning methods for drug-target affinity (DTA) prediction have shown great promise in accelerating drug discovery and reducing development costs. Although graph neural networks have improved drug representation learning for DTA prediction tasks, many models still struggle to effectively and efficiently

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Scientific software is often locked in its source language, callable from others but not natively extensible or built upon. We present Bio-Babel (biobabel.stanford.edu), an autonomous framework that rebuilds software natively in target ecosystems and makes it agent-callable, shipping each package with an agent-readable contract of its usage. Reconstructing a hierarchical, interdependent R-to-Python single-cell stack,

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Brain-computer interfaces translate neural activity into device commands, yet their performance hinges on preprocessing that remains manual, expert-dependent and poorly reproducible. Large language model agents can automate scientific coding, but existing systems lack the modality coverage, raw-data isolation, experience accumulation and domain oversight that neural preprocessing requires. We introduce EasyBCI, a two

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Malaria remains a significant global health burden, necessitating continuous research efforts to understand its complex molecular mechanisms, epidemiology, and potential therapeutic interventions. Extracting essential biomedical information from the vast and constantly growing malaria literature is a challenging task that demands innovative approaches. Recently, pre-trained language models have revolutionized natural

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Nature Biotechnology, Published online: 02 September 2026; doi:10.1038/s41587-026-03212-2 A machine-learning framework called symclatron uses genome sequencing content to predict whether uncultivated bacteria and archaea live independently or in close association with hosts. Applied to a global genome collection, it suggests that host-dependent microbes are widespread across Earth’s biomes and microbial phyla.

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Nature, Published online: 02 September 2026; doi:10.1038/s41586-026-10952-3 Synthetic virus-like protein architectures designed by artificial intelligence show superior ability to deliver RNA into cells, compared with their naturally occurring counterparts, by overcoming the evolutionary constraints limiting viral evolution.

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Accurate, reproducible interpretation of kidney allograft biopsies is critical for diagnosis of graft injury to guide prognosis and management. The international Banff classification is a consensus diagnostic system based on semiquantitative histological lesion scoring on either extent or severity of kidney transplant biopsies. However, pathologist scoring is limited by substantial interobserver variability, constrai

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Pediatric low-grade glioma (pLGG) is the most common type of brain tumor in children, accounting for approximately 30% of all central nervous system tumors in children. pLGG has multiple molecular subtypes that differ in disease progression, recurrence patterns, and treatment responses. Conventional wet lab approaches including molecular profiling and histopathological studies for pLGG characterization are time consu

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Protein co-abundance clusters preserved across tissues can reveal shared disease mechanisms and candidate therapeutic targets, particularly when proteins implicated in organ-confined diseases converge in peripheral or accessible tissues. However, previous cross-tissue studies have focused on biologically pre-selected tissue pairs, leaving most possible combinations and non-obvious relationships unexplored. We present

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Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hy

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Scientific agents can produce plausible answers while remaining unable to establish whether the computation behind an answer is executable, recoverable, or reproducible. We present BloClaw, an AI4S workstation built around a simple principle: a scientific agent should know what it can do, show how it did it, and state what remains unvalidated. Each capability declares an execution state, input constraints, dependenci

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Phage tail-like elements (PTEs) -- tailocins, bacterial type VI secretion systems (T6SS), and extracellular contractile injection systems (eCIS) -- are contractile nanomachines that bacteria use to kill their neighbors and compete within their micro-ecosystems. PTEs help shape microbial community composition. Most PTE detection tools only detect a single PTE class. Moreover, most tailocin detection methods are largel

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AI agents for therapeutic reasoning across biological contexts - Michelle M. Li, Carnegie Mellon University - Microsoft Research New England Generative Modeling & Sampling Seminar Therapeutic hypotheses can transfer across diseases but their relevance depends on biological context. The same target, perturbation, or treatment can produce different effects across cell types, disease states, genetic backgrounds, and pat

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At a glance - The Flash family extends GigaPath and GigaTIME with dramatically improved efficiency, making large-scale pathology research more accessible and practical. - A distilled pathology foundation model backbone reduces computational requirements without sacrificing performance, enabling repeated analyses across larger patient cohorts. - These open models support population-scale discovery, helping researchers

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Sparse autoencoders (SAEs), and other interpretability methods could turn AI models in Biology and other fields into engines of scientific discovery by explaining the superhuman capabilities of those models. However, a latent is only useful if we know three things: whether it is coherent, whether it can be described, and whether that description has predictive power. These questions are routinely conflated. We assemb

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Objective To measure the accuracy of clinical coding, clinician review, and a locally deployed large language model (LLM) in identifying alcohol, drug, and self-harm involvement in emergency department (ED) attendances, and quantify prevalence. Design Two-phase diagnostic accuracy study. In a validation week, the identification strategies were assessed against a conflict-adjudicated reference standard (n=2,256); the

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The widespread adoption of clinical large language models (LLMs) introduces significant risks of automation bias, premature closure, and clinician deskilling. Current interpretability paradigms, including latent space trajectories, Concept Activation Vectors, and Concept Bottleneck Models, suffer from topological stagnation, metric distortion, and epistemic occlusion, frequently masking intermediate diagnostic uncert

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In large data sets discovery is often limited to pre-conceived hypotheses and data fishing. Here we tested whether systematic exploration of AI generated hypotheses could uncover clinically meaningful signals in extensively studied data. We deployed AutoDiscovery, a newly launched large language model (LLM) framework designed to search for hypotheses based on surprisal and systematically interrogate complex datasets,

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Nature Biotechnology, Published online: 31 August 2026; doi:10.1038/s41587-026-03213-1 Symbiotic relationships of microorganisms are predicted and cataloged with a machine learning tool.

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Protein language models (pLMs) encode information about protein sequences which enable downstream tasks such as structure prediction, but their internal representations are not well understood. Sparse autoencoders (SAEs) provide a promising tool to disentangle latent pLM representations into interpretable features, but existing annotation pipelines largely rely on protein-level annotations derived from database label

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The analysis speed is up to 25 times faster Strategic technology to escape under Big Tech Bio-artificial intelligence (AI), such as Google DeepMind's "Alpha Fold 3," which won the 2024 Nobel Prize in Chemistry, has also emerged in Korea. The speed of predicting protein structure and designing new drug candidates is the fastest in the world. KAIST, the organizer of the 'AI-specific foundation model project' promoted b

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Ai2 and Providence Swedish Cancer Institute partner to advance AI-assisted scientific discovery The collaboration includes clinically meaningful research findings on lobular breast cancer, and a local deployment to enable next-gen discovery while keeping clinical data secure. This partnership represents an important milestone for AutoDiscovery as it moves beyond public research datasets into active cancer research pr

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Multimodal protein language models (pLMs) learn joint protein sequence-structure distributions, and their generation performance should also depend critically on inference-time sampling strategies. Yet prior work has focused more on model training than on how inference-time strategies behave. In this paper, we establish a three-stage investigation framework to empirically study the inference design space of multimoda

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Recently, there has been a growing adoption of protein language models (PLMs) in biomedical science. Their embeddings provide a rich numerical representation of protein sequences which achieve state-of-the-art performance on several downstream tasks including protein fitness prediction. However, PLM embeddings are not directly interpretable and, thereby, it remains unclear what features they encode. To gain insight i

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Artificial intelligence (AI) promises to accelerate biological research by automating computational analyses. Yet the ability of AI agents to carry out computational biology at the scale of complete research studies has not been systematically evaluated. Here we introduce BixBench3, a benchmark that measures the capacity of AI agents to process raw biological data through to scientific results. We designed BixBench3

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An accurate and tractable approximation of the single-point mutation-induced change in protein thermodynamic stability, denoted by DDG, is critical for understanding the genotype-phenotype relationship. Several computational methods have been proposed for this problem; however, limited and error-prone training data and the difficult-to-predict magnitude of structural perturbations make this a challenging task. Conseq

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Background Large language models are increasingly proposed to post-edit decoded text in communication brain-computer interfaces and augmentative communication. A fluent model can substitute a different intent than attempted (intent drift). Whether meaning survives or confidence flags failure is unmeasured. Methods In-silico benchmark of 20 open-weight models post-editing text (4,252,326 labeled generations) corrupted

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August 26, 2026 Ahmed A. Metwally, Staff Research Scientist, and Zechen Li, Student Researcher, Google Research GlucoFM is a lightweight, self-supervised CGM foundation model that models slower glucose trends and short-term deviations in separate streams, producing transferable representations and setting new performance standards across diverse metabolic prediction tasks, including diabetes risk assessment, insulin

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Large language models (LLMs) have shown strong capabilities in reasoning, planning, and tool use, but many scientific and engineering tasks require more than plausible text and code generation. They require understanding how a system responds to intervention, which in practice depends on controlled experimentation. In this work, we propose a multi-agent framework that enables LLM agents to conduct controlled experime

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Messenger RNA (mRNA)-based therapeutics have emerged as a powerful platform for vaccines, protein replacement therapies, and cancer immunotherapy. A critical bottleneck in mRNA development is manufacturing large quantities of RNA economically, as measured by RNA yield emerging from an in vitro transcription (IVT) reaction. However, how promoter-adjacent DNA sequences influence RNA yield remains poorly characterized.

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Nature Machine Intelligence, Published online: 26 August 2026; doi:10.1038/s42256-026-01286-w Feng et al. introduce MAP, an artificial intelligence framework that integrates biological mechanism knowledge to predict how cells respond to chemical perturbation, improving generalization to untested drugs and prioritizing cancer drug candidates in virtual screening.

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Abstract Neural network potentials (NNPs) can provide insight into biological processes at atomic resolution. Training these NNPs requires large and diverse datasets of molecules, conformations, and configurations. However, so far little attention has been paid to the description of solvation, despite its importance for biomolecular systems. This work lays the foundation for NNPs where solvation is an integral part o

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Abstract Knowledge of RNA sequences, expression, splicing, isoforms, structure and modifications is central for understanding and targeting cellular processes. Revolutionary single-cell and spatial transcriptomics technologies—for example, as deployed by consortia such as the Human Cell Atlas—partially capture this diversity and generate cellular profiles that expand at petabyte scale each year 1–5 . Yet researchers

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Nature Computational Science, Published online: 26 August 2026; doi:10.1038/s43588-026-01026-5 PULSE is an AI framework that uses patients’ longitudinal electronic health records to accurately generate within- and cross-modal data from routine clinical laboratory tests, enabling cost-effective precision medicine across diverse biomedical settings.

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Nature Computational Science, Published online: 26 August 2026; doi:10.1038/s43588-026-01024-7 Most patients leave the clinic with only a few of the clinical measurements needed to tell the full story of their health. PULSE is a new artificial intelligence (AI) framework that fills in the blanks, using a patient’s own history to predict what was never directly measured.

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The standard genetic code uses 64 codons to encode 20 canonical amino acids across domains of life. New-to-nature genetic codes enable new chemistries, therapeutics and ecosystem engineering, but recoding the genome of an organism is exceptionally challenging 1-6 . Here we describe automated genetic tRNA expansion (AGENTEX) for multiplexed robotic prototyping of genetic codes in cell-free translation systems. Two Wat

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Pretrained genome language models provide reusable representations for DNA sequence analysis, but turning them into reliable downstream predictors remains non-trivial. Their practical performance depends strongly on fine-tuning recipes, and default recipes reported in prior studies may be suboptimal for new tasks or model backbones, making weak downstream results difficult to interpret. These requirements place a sub

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Protein-peptide interactions are central to cellular regulation and peptide-based drug discovery, yet existing computational methods mainly focus on interaction classification, binding-site prediction, or peptide binder generation. These formulations provide limited insight into the physicochemical mechanisms that determine how a peptide binds to a protein. In this work, we introduce \textbf{PepLLM}, an instruction-t

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High-quality molecular datasets are essential for reliable machine learning in cheminformatics and bioinformatics, yet dataset quality is rarely assessed systematically and its relationship with downstream model performance remains poorly understood. Here, we present MolJam, an open-source framework for quantitative assessment of molecular dataset quality across five dimensions-structural integrity, data quality, exp

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Bacteriophage lytic enzymes and depolymerases are relevant to phage biology, antimicrobial development, and protein engineering, but their sequence and annotation data remain dispersed across general databases, specialized resources, genome-centred collections, and prediction-oriented datasets. We present PhageLysData, an evidence-aware and AI-ready resource constructed through reproducible multisource integration, p

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Protein language models (PLMs) learn evolutionary information from large-scale sequence data, but three-dimensional relationships are encoded only implicitly. Here, we introduce Prot-LAMBDA (Protein LAnguage Model Boosted with Distance Awareness), a PLM that explicitly incorporates spatial relationships by coupling residue embeddings with inter-residue contacts. Prot-LAMBDA improves performance across diverse structu

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Predicting final ischemic infarct volumes from acute imaging is a cornerstone of personalized stroke management, yet current strategies remain polarized between uninterpretable machine learning architectures and overly detailed electrophysiological models that are intractable in clinical imaging settings. We bridge this clinical gap by introducing the first imaging-driven framework that parameterizes a Fisher-KPP rea

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Inferring continuous system evolution from sparse temporal snapshots is a key challenge in generative modeling and single-cell omics. While Optimal Transport (OT) is popular, existing frameworks are largely restricted to first-order dynamics, assuming memoryless velocity fields. This limits expressiveness, as first-order systems fail to account for regulatory momentum and time-delayed responses inherent in processes

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Nature Biotechnology, Published online: 24 August 2026; doi:10.1038/s41587-026-03272-4 Computational protein evolution with sparse data generates efficient small RNA-guided nucleases.

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Background and Objective: Early and reliable disease prediction from structured clinical data remains challenging when datasets are small, highly imbalanced, and contain limited positive disease cases. Conventional machine learning (ML) and deep learning approaches often struggle to capture clinically meaningful relationships under such low-data representation conditions due to weak statistical associations between f

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Off-target cleavage is a central safety concern for CRISPR-Cas9 genome editing, particularly in therapeutic applications where unintended double-strand breaks carry clinical risk. We benchmarked five machine learning classifiers: logistic regression on mismatch-count summary features, a random forest and a gradient boosting model on one-hot-encoded sgRNA/candidate-site sequence pairs, a one-dimensional convolutional

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Objective Translating free-text clinical trial criteria into computable code sets is a valuable standardization practice that is necessary for producing reproducible real-world evidence studies but requires standardized interpretation across multiple clinical vocabularies. Methods We developed TrialCode Agent, a hybrid-large language model (LLM)-terminology verification agent that generates, formats, verifies, and ex

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Accurate computational prediction of enzyme function, standardized by Enzyme Commission (EC) numbers, is essential for large-scale genome annotation and generative enzyme design. However, it remains unclear whether state-of-the-art predictors learn the intrinsic structural determinants of catalytic activity or merely rely on global sequence similarity to annotated homologues. To address this gap, we introduce EnzymAR

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Summary: Dynamic scientific machine learning (SciML) models that combine mechanistic ordinary differential equations (ODEs) with machine learning (ML) components have applications ranging from learning unknown biological processes to integrating auxiliary data modalities into dynamic modelling. To enable reproducible and efficient SciML training, we introduce PEtab SciML, an interoperable data format for specifying p

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Bioinformatics software and databases are essential components of modern life science research, yet their mentions in the scientific literature are often inconsistent and difficult to systematically identify at scale. The lack of a comprehensive and up-to-date catalog of bioinformatics resources hinders efforts toward automated biomedical knowledge extraction and streamlined data analysis. Here we present SNAIL, a hy

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This study evaluated the performance of Transformer-based models in predicting hepatitis C virus (HCV) genotypes and subtypes after fine-tuning. A total of 2,881 HCV whole-genome sequences from the Los Alamos HCV Sequence Database were used, covering genotypes 1 to 6 and all confirmed subtypes; genotypes 7 and 8 were excluded due to insufficient samples. Fine-tuning with homogeneous fragmentation and balanced sample

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Microsoft Research's Skala 1.1 demonstrates the continuous improvement of their deep-learning DFT approach. Trained on 2.5× more data than its predecessor, it delivers substantially higher accuracy across key molecular simulation challenges, including thermochemistry, reaction kinetics, and molecular structure prediction. Skala is now available in CP2K and is being integrated into Psi4, FHI-aims, ORCA, and VASP, brin

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Missense mutations and post-translational modifications (PTMs) are major molecular perturbations that reshape protein function but are traditionally studied independently. Current computational approaches largely rely on sequence conservation or static structural features, limiting our understanding of how perturbations alter intrinsic protein dynamics. We present DynGeo-Pheno, a unified geometric deep learning frame

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What happens when you teach an LLM-based agent the scientific method? Motivation: Scientific discovery emerges from cycles of hypothesis, implementation, empirical testing, and feedback. Can this process be automated? We approach automated algorithm design through the lens of the scientific method, where an LLM-based agent goes through each step of the process in an ordered, iterative fashion. Results: We present The

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Combining the answers a large language model (LLM) samples for a question into one decision is a test-time information fusion problem, usually solved by majority voting. Voting is unreliable on difficult questions, where the sampled answers share correlated errors, so the wrong answer can win and drawing more samples makes the decision worse. Selecting a candidate by reading a correctness signal from the model's hidd

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Drug response prediction (DRP) models are an active area of research in pharmacogenomics, with growing potential to accelerate the identification of effective anticancer drugs. However, their predictive performance is often constrained by limited dataset scale and insufficient coverages of cancer and chemical spaces. In addition, inconsistent benchmarking practices hinder reliable comparison across models. Standardiz

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Nature Machine Intelligence, Published online: 19 August 2026; doi:10.1038/s42256-026-01291-z Chen, Wang, Li et al. introduce VITAL, a dual-channel deep learning framework that co-learns sequence and structural contexts to quantitatively predict peptide–protein interactions, map binding interfaces and estimate affinity.

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Perturbational transcriptomics is a powerful tool for understanding gene function and drug effects, yet predicting how perturbations manifest across different biological contexts remains a central challenge, limiting translation from model systems to clinically relevant tissues. Despite growing interest in this problem, benchmarking efforts have been hindered by inconsistent evaluation tasks, heterogeneous metrics, a

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Abstract Background: Cardiovascular-kidney-metabolic (CKM) syndrome is an increasingly prevalent multisystem condition associated with morbidity, fragmented care, recurrent hospitalization, and rising healthcare costs. While cardiovascular risk models estimate future disease risk, fewer frameworks support multidisciplinary CKM care, clinician decision-making, and population health management. Synthetic data environme

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In this post, we share two results that show how Claude can help life scientists increase the pace of their research. In the first, we tested Claude’s ability to design protein binders from scratch, a key step in creating protein-based drugs that has historically taken a specialist weeks or months per target. In the second example, we evaluated whether Claude can accelerate chemical analysis. Claude Opus 5, a general

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When a model reads a drug's class from its name—not its knowledge August 18, 2026 Ai2 People increasingly bring health and medication questions to AI models, often naming specific drugs. But a model can sound knowledgeable without knowing much about the drug itself. Many drug names encode their class through affixes – often suffixes like -pril, -olol, and -azoline – giving a model a shortcut: infer the answer from th

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Large language models (LLMs) are increasingly evaluated and used in medicine, but clinical usefulness depends on answer accuracy and whether confidence tracks evidence quality and uncertainty. We developed a controlled, psychophysics-inspired clinical benchmark to test diagnostic choice and confidence behavior in a medical LLM. The benchmark focused on probable Alzheimer-type neurocognitive disorder (AT-NCD) versus d

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Nature reports that robotic supports, once designed for clinical use, are now being developed as cheaper, smaller devices aimed at everyone — from people with ageing knees to twenty-somethings seeking to climb mountains.

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AlphaFold3 (AF3) predicts protein-complex structures from sequence with near-experimental accuracy on many targets, substantially lowering the cost of mechanistic and therapeutic discovery. However, application to antibody epitope prediction is hampered by an approximately 63% failure rate. Comparing successful and failed AF3 predictions across antibody-antigen and nanobody-antigen complexes, we found that failed pre

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Synaptic vesicle glycoprotein 2C (SV2C) is a vesicular protein enriched in dopaminergic neurons of the basal ganglia that modulates dopamine storage and release, and its disruption is implicated in Parkinson's disease (PD). Despite strong genetic and pathological links to PD, there are no selective small-molecule probes for SV2C. Here, we describe an AI-enhanced virtual screening (VS) and experimental campaign that i

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Arc Institute’s Patrick Hsu on Building an App Store for Biology with AI Patrick Hsu, co-founder of Arc Institute, discusses the opportunities for AI in biology beyond just drug development, and how Evo 2, their new biology foundation model, is enabling a broad ecosystem of applications. Evo 2 was trained on a vast dataset of genomic data to learn evolutionary patterns that would have taken years to find; as a result

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Nature Methods, Published online: 14 August 2026; doi:10.1038/s41592-026-03138-2 Direct preference optimization (DPO) aligns a pretrained protein language model with experimental stability data, yielding ProteinDPO, a model that scores and generates thermostable protein sequences. Applied to H5N1 influenza hemagglutinin, it achieved large improvements in thermal stability of hemagglutinin while preserving antibody re

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Nature Methods, Published online: 14 August 2026; doi:10.1038/s41592-026-03137-3 This Article demonstrates that direct preference optimization (DPO) can be used to effectively align an unsupervised structure-conditioned language model with biophysical information. The aligned model, ProteinDPO, achieves stability prediction competitive with that of task-specific models and consistently outperforms unsupervised and fi

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Microsoft Research introduces CARE-X, a unified chest X-ray vision-language model (VLM) designed for diverse clinical interpretation tasks. It combines free-text generation with structured prediction to provide both narrative reports and deterministic outputs. The model uses reinforcement learning (DAPO) to reward clinical correctness in a multi-task setting. In a separate research experiment, the team paired Qwen3-V

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By 2050, antibiotic-resistant infections could be associated with over eight million deaths annually worldwide. Developing new antibiotics is slow and costly, but generative AI models, guided by scientists, can propose novel molecules, and physics-based simulations can quickly assess their potential as drugs. Peptides like insulin and vancomycin serve as important starting points. A good AI model includes a generator

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An important article by field experts points out that despite widespread AI application in drug discovery, evidence of clinically relevant impact remains 'disappointingly limited'. They conclude we are at a stage of 'absence of evidence', not necessarily 'evidence of absence'. Desired outcomes like better decision-making in selecting disease areas, targets, lead molecules, and trial designs are among areas current AI

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A joint study by Insilico Medicine, the University of Copenhagen, and academic partners identifies NDRG1 as a key regulator of DNA damage repair, demonstrating that targeting this pathway via the antimalarial drug quinacrine triggers synthetic lethality in specific cancer subtypes. Insilico Medicine (HKEX:3696, "Insilico"), a clinical-stage biotechnology company pioneering generative artificial intelligence (AI) for

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Anthropic updated its Fable 5 biology safeguards on August 7, the same day researchers from Stanford and the Arc Institute published a paper in Science demonstrating that their Evo 1 and Evo 2 models can design fully functional viral genomes. The timing is not coincidental. The question Anthropic is answering is not whether AI can do biology — Stanford just proved it can — but who gets to control the terms of access.

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Collaborative Study with University of Chicago and Johns Hopkins University Establishes First Multi-Omic Map and Actionable AI Targets for Rare Head and Neck Malignancy CAMBRIDGE, Mass. — Aug 4, 2026 — Insilico Medicine (“Insilico”), a clinical-stage generative artificial intelligence (AI)-driven biotechnology company, today announced the publication of a collaborative study in the journal npj Precision Oncology. The

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Sarah Urbut is a physician-scientist at Harvard Medical School and the Massachusetts General Research Institute. She and her colleagues published a landmark paper in Nature on patient health journeys for 3 different cohorts—the Mass General Brigham patients, the UK Biobank, and the All of Us research program—with a cumulative 683,000 individuals, 348 diseases, and up to 52 years of follow-up. Integrating electronic m

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In healthcare settings, LLM performance differs between evaluation and deployment, with a recent study finding a 61 percentage point gap. The authors argue this gap arises not from poorly designed benchmarks but from implicit assumptions in evaluation protocols that fail at deployment. They propose a taxonomy categorizing assumptions into task and outcome types to diagnose where the gap arises and what is needed to c

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Liu et al. present a physics-induced deep-learning approach that extracts tissue composition information from routine single-energy CT scans. By incorporating X-ray imaging principles into model design, this approach improves material characterization and enhances performance across diverse clinical tasks. The framework expands the diagnostic value of standard CT imaging and supports more accessible precision medicin

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Researchers at ISTA and international collaborators have developed a method to guide AlphaFold with experimental data, enabling the modeling of protein structure ensembles that better reflect physical and biological reality. Although AlphaFold predicts protein 3D structures with remarkable accuracy, it tends to reduce heterogeneous structures to a single dominant conformation and overlooks experimental conditions aff

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UniPert-G2CP is an AI framework for jointly modeling molecular-level perturbation causes with phenotypic-level cellular effects across genetic and chemical domains. These complementary views enable more accurate perturbation prediction, mechanistic discovery, and efficient in silico drug screening.

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In April 2026, a NASA Artemis II photo of Earth caught public attention, but AI can fabricate similar images in seconds, raising trust issues. The author, a visual science communication researcher, notes that AI-generated images infiltrate science: used for illustrations, synthetic data, editing lab images, and outreach, blurring lines between illustration, enhancement, and fabrication. In 2024, two papers were retra

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Protein language models have excelled at modeling individual proteins, but extending these capabilities to protein complexes remains a major challenge. MSA Pairformer, a parameter-efficient protein language model, generalizes to protein-protein interactions despite being trained exclusively on monomers, enabling accurate prediction of interface variant effects and subfamily-specific homo-oligomeric binding modes.

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“The future of biology is programmable.” To give you a sense of how important this topic is, Patrick Hsu is the first guest we’ve had back to Ground Truths since the podcast began a few years ago. Nearly 2 years ago we spoke about digital biology but so much has happened since to help unravel the complexities in the language of life. Patrick is a co-founder and core investigator at the Arc Institute . His work is at

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AI-guided protein design was experimentally tested as a way to improve the efficiency and targeting range of compact base editors.

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The work audits AI protein structures at the level of electrostatic interactions, beyond aggregate structure scores.

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Enzymes drive cellular metabolism, yet predicting catalytic properties from amino acid sequences remains challenging. Existing protein language models (PLMs) provide powerful general-purpose representations but are often inefficient for high-throughput screening and insufficiently adapted to enzyme-specific tasks. Here, we propose EnzGFM, an enzyme-specific PLM based on a Mamba-Transformer hybrid architecture with hi

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AI-assisted fragment prioritization was connected to NMR and X-ray evidence rather than evaluated only in silico.

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Our approach to bioresilience The global biosecurity landscape is rapidly evolving. Shifting natural ecosystems, global travel and the potential misuse of AI require greater vigilance — yet AI is also a critical tool for our response. We need frontier AI models, and the scientific advances they will enable, to respond to these challenges and help make society more resilient to events like future outbreaks. Today, Goo

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AlphaGEM uses structural information and deep learning to fill gaps in genome-scale metabolic models.

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Fluorescent proteins and small-molecule dyes offer complementary advantages for biological imaging: Proteins are amenable to genetic tagging, whereas dyes provide superior brightness and photostability. To combine these strengths, we used de novo protein design to generate small, nanomolar-affinity, high-selectivity binders (NovoTags) for three cell-permeable dyes spanning the visible spectrum. We show that the NovoT

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Therapeutic peptides combine the advantages of small molecules and antibodies, offering target flexibility and low immunogenicity, yet their successful translation requires careful evaluation of multiple developability properties beyond binding alone. As chemically modified peptides become increasingly common in drug design, no unified platform currently supports systematic property assessment across both canonical s

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“I have this idea of a communal brain.” David Baker, PhD, told me as I sat in his office at the University of Washington (UW) surrounded by colorful and complex figurines of protein structures. It was the one-year anniversary of his Nobel Prize in Chemistry win. Just outside his doors, a lab of more than 100 researchers was united by the shared ambition to design proteins from scratch (or de novo) for powerful applic

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Protein language models, as well as models that incorporate structural information or homologous sequences, estimate the sequence likelihood p(sequence), which reflects the protein fitness landscape and is commonly used in mutation effect prediction and protein design. It is widely believed in deep learning field that larger models perform better across tasks. However, for fitness prediction, language model performan

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This week Tony Wyss-Coray and I published an invited review paper in Nature Medicine entitled “Biological Aging Clocks in Health and Disease.” The concepts and content of the paper may be difficult to understand for people not working in life science, but they are groundbreaking and have exciting implications for the future of medicine. So this edition of Ground Truths is intended to make all of the progress comprehe

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NicheTrans uses spatial context to translate between omics modalities and preserve tissue heterogeneity.

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Biohub researchers performed what they believe is the first genome-wide CRISPR study of primary human adult skin cells, then used an AI model to mine the results for overlooked drug targets for psoriasis. They found an unlikely candidate: the receptor for oxytocin, a hormone involved in childbirth, and sometimes called the “love hormone” for its additional role in social bonding. As reported in Nature Communications,

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A cross-species foundation model creates a shared cell representation that supports zero-shot transfer across biological datasets.

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Introduction Timestamps Transcript Apple Podcasts Spotify Introduction Back in early May, I flew to North Carolina to interview Alex Rubinsteyn (left) and Benjamin Vincent (right). Together, they run the Personalized Immunotherapy Research Lab (PIRL) at UNC-Chapel Hill. Alex and Benjamin are both extraordinary people, demonstrating a degree of translational ambition, scientific insight, clarity of thought, and just g

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Croonian Prize Lecture: Advances in computational protein design Join us for the Croonian Prize Lecture delivered by Dr David Baker. The Croonian Medal and Lecture 2026 is awarded to Dr David Baker for pioneering the design of completely new proteins as well as playing key roles in the development of protein structure prediction and protein engineering tools. Proteins mediate the critical processes of life and beauti

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LifeSciBench evaluates models on research tasks designed with life scientists rather than relying on generic knowledge questions.

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Aviv Regev is a computational biologist who leads Genentech Research and Early Development. Previously a professor at the Massachusetts Institute of Technology and a leader at the Broad Institute of M.I.T. and Harvard University, her research has advanced single-cell genomics, systems biology and large-scale efforts to map human cells. [This interview was edited for length and clarity.] How would you describe the cur

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In the journal Cell , a team of >80 researchers from 4 continents reported on the discovery of a 14-protein blood test which paved the way for predicting and preventing lung cancer more than 5 years before it would be diagnosed. Current efforts in cancer are largely directed to therapy and early detection, with only a very limited foray to prevention. In a recent Ground Truths , I reviewed the emerging potential for

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XtalPi has entered a strategic partnership valued at more than $400m with an international biopharmaceutical company to develop oral small-molecule therapeutics targeting a G protein-coupled receptor (GPCR). The collaboration follows a successful pilot phase, where XtalPi’s integrated quantum physics and AI algorithms achieved improved hit rates, highlighting the platform’s capability to address the complex metabolic

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Share - Findings from insitro’s lead therapeutic CTRO-1013 program demonstrate that liver-targeted silencing of IRS1 lowered circulatory biomarkers associated with fibrosis progression and hepatocyte injury in an industry-standard fibrogenic preclinical mouse model of MASH - Data presented today at ADA provide converging evidence from human genetics and preclinical models supporting IRS1’s effect on liver fat and liv

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Science Jun 8, 2026 Written by Laura Luebbert. Based on research by Ferdous Nasri, Sarah Gurev, Patrick Varilly, Krithik Ramesh, Nuala A. O’Leary, Jonah Cool, Bernhard Y. Renard, Pardis Sabeti, and Laura Luebbert. In this post, Laura Luebbert argues that we need to make biological data infrastructure more agent-friendly. As a case study, she and her team tasked scientific research agents (Claude, Biomni Open Source (

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A claim has emerged that a new protein structure prediction artificial intelligence (AI) model surpasses the performance of Google DeepMind’s AlphaFold, which was developed by DeepMind and even led to the 2024 Nobel Prize in Chemistry. Experts are divided between seeing it as an important new resource for protein structure prediction and expressing doubts about its performance. According to a report in the internatio

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REDWOOD CITY, Calif., May 27, 2026 – Biohub today announced the release of a world model of protein biology: a scientific engine for prediction, design, and discovery that can map proteins across the tree of life, predict their structures, and design new protein binders that function in laboratory experiments. Proteins are the machinery of life. Nearly every function of the human body depends on them. They are among

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Demis Hassabis, CEO of Google DeepMind and 2024 Nobel laureate in Chemistry, has spent the first months of 2026 articulating a specific and measurable thesis: AlphaFold, the protein-structure model that now serves more than three million researchers across 190 countries, is the first rung in an AI-powered scientific revolution, not the last. His clearest statement of this position came in a Semafor interview publishe

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The advent of the evolutionary scale modeling (ESM) series of protein language models (PLMs) is a significant innovation in the convergence of large language models (LLMs) with protein representation. These models, trained on large amounts of unlabeled protein sequence data, learn the intricate patterns of mutation and conservation that have sculpted protein families through evolutionary history. ESM has become a wid

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Wang et al. introduce MINT, a framework that transfers knowledge from multimodal biomedical models to unimodal large decoder models through preference optimization. Applied to rare-disease prediction and tissue-type classification, MINT improves task performance while preserving general reasoning ability and offers a practical strategy for extending scarce multimodal biomedical knowledge to more flexible foundation m

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Science Apr 29, 2026 In this post, Brianna, a researcher on the discovery team, shares results from a recent bioinformatics benchmarking effort. Almost as soon as large language models could hold a conversation, people started asking how they’d stack up against human experts. Could models pass the bar exam? Could they answer medical licensing questions, or solve Olympiad math problems? Such benchmarks—self-contained

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LADDER is a deep learning framework designed for predicting tethering of chromatin to the nuclear lamina via DNA sequence and associated genomic properties. By combining it with genome-wide random in silico deletions, the authors revealed an intrinsic genome lamina susceptibility landscape. LADDER can learn general features of lamina organization from one species and use them to extrapolate predictions to other mamma

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Explaining deep neural network predictions on genome sequences enables biological insight and hypothesis generation-often of greater interest than predictive performance alone. While explanations of convolutional neural networks (CNNs) have been shown to capture relevant patterns in genome sequences, it is unclear whether this transfers to more expressive Transformer-based genome language models (gLMs). To answer thi

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Share Amyotrophic Lateral Sclerosis (ALS) presents one of the most formidable challenges in medical science. For too long, the search for effective treatments has been hampered by a temptation to revisit familiar hypotheses and established biological pathways. While understandable given the slow progress and frequent setbacks, this approach rarely advances the science. At the heart of insitro’s collaboration with Bri

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Collaboration expands to include two additional therapeutic targets for the treatment of ALS discovered via insitro’s AI-driven Virtual Human™ platform Joint effort focuses on identifying key biological drivers to deliver disease-modifying interventions for ALS patients SOUTH SAN FRANCISCO, Calif.--(BUSINESS WIRE)--insitro, the AI therapeutics company built on causal biology, today announced the expansion of its stra

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James Zou | Associate Professor of Biomedical Data Science, Stanford University, California, USA Citation: EMJ. 2026;11[1]:28-31. https://doi.org/10.33590/emj/OR9BWSEN When did you first become interested in applying AI to clinical research? I’ve been working at the intersection of AI and biomedicine for about 15 years now, since my PhD. My initial work was more focused on using AI to enable biomedical discoveries. W

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Despite the recent release of numerous protein language models, relatively little work has addressed how to best sample from them to optimize desired biological properties. This paper fills that gap by proposing a flexible and effective sampling method for masked language models (MLMs), and by systematically evaluating models and methods both in silico and in vitro in the context of antibody therapeutics campaigns. T

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OpenAI’s GPT can summarize research papers and make predictions—but can it do science? Can it generate hypotheses, design experiments, interpret results and iterate? Last summer researchers at OpenAI and Ginkgo Bioworks, a company that designs and installs autonomous, robot-run labs, decided to find out. Though artificial intelligence systems have posted high scores in math, physics and computer science, biology is h

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Optimizing molecules to achieve desired properties is a central bottleneck across the chemical sciences, particularly in the pharmaceutical industry, where it underlies the discovery of new drugs. Since molecular property evaluation often relies on costly and rate-limited oracles, such as experimental assays, molecular optimization must be highly sample-efficient. To address this, we introduce SEISMO, an LLM agent fo

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A vaccine called GBP511 has begun clinical testing in Australia. Pioneered by UW Medicine researchers, it is intended to protect against COVID-19 and related coronaviruses — including some that haven’t yet jumped to humans. South Korean pharmaceutical company SK bioscience is bringing the vaccine to trial. “GBP511 is the first vaccine to reach human testing that is intended to protect against multiple strains of the

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Share How insitro unlocked population-scale human genetics in a tissue that has resisted measurement for decades – and what it reveals about peripheral approaches to metabolic disease The Furnace We Couldn’t See The human body contains a type of fat tissue engineered to burn energy rather than store it. Brown adipose tissue – dense with mitochondria, rich in uncoupling proteins – converts glucose and lipids into heat

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Share AI-enabled brown adipose tissue (BAT) phenotype unlocks population-scale human genetic discovery BAT-01 knockdown drives 15% body-weight reduction in preclinical models through peripheral adipose beiging, preserving lean mass Key findings presented at the Keystone Symposia on Obesity Therapeutics SOUTH SAN FRANCISCO, Calif. – Feb 2, 2026 – insitro, the AI therapeutics company built on causal biology, today repo

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Share TherML™ integrates CombinAbleAI’s advanced, next generation biologics expertise with insitro’s existing small molecule and oligonucleotide capabilities, unifying therapeutic design across all major drug modalities in a single AI engine SOUTH SAN FRANCISCO, Calif. – Jan. 12, 2026 – insitro, the AI therapeutics company built on causal biology, today announced the acquisition of CombinAbleAI and the launch of insi

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Share The primary driver of clinical attrition in drug discovery is often not insufficient scientific rigor, it is misalignment between the biological target and the therapeutic intervention. Discovery organizations have historically structured themselves around technological capabilities, operating as either small molecule, biologics, or oligonucleotide specialists. This specialization creates an underappreciated co

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insitro announced it will acquire CombinAbleAI and, alongside the deal, has launched TherML (Therapeutic Machine Learning), a platform it describes as a single, modality-agnostic AI engine for drug discovery and design. The deal is expected to close in late January 2026 and is intended to expand insitro’s capabilities beyond its existing small-molecule and oligonucleotide work into antibodies and other complex biolog

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That enormous potential is what attracted Christina Savvides, ’29, to the IPD. She’s working toward a UW medical degree and a doctorate in molecular engineering, thanks in part to the Oren Traub Endowed Fellowship, which supports students enrolled in the UW School of Medicine’s Medical Scientist Training Program. “I want to be what is called a physician scientist,”says Savvides, who chose the UW for its top-notch pro

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Share New publication demonstrates that insitro’s POSH platform — integrating pooled CRISPR screening and self-supervised deep learning — successfully breaks the historic compromise between scale and depth to more rapidly identify novel therapeutic targets. SOUTH SAN FRANCISCO, Calif. — Dec. 16, 2025 — insitro, the AI therapeutics company built on causal biology, today announced the publication of research in Nature

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For decades now, it’s been fairly well established that once you turn 40 you should start paying more attention to your body. That’s when women are supposed to start getting mammograms and men are supposed to start paying a bit more attention to their prostates. Over the next decade, you’ll start getting colonoscopies, and from then on out, it feels like a gradual march of doctor’s appointments and tests until your b

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Today we are releasing RFdiffusion3 as open-source software. This state-of-the-art AI model for biodesign is capable of generating new proteins that interact with any type of molecule commonly found inside living cells. “We built this as a general model and are sharing it freely so the scientific community can create things we haven’t even imagined yet.” Lead developer Rohith Krishna, PhD RFdiffusion3 tackles researc

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Today we report the development of AI-generated protein catalysts that function efficiently immediately upon synthesis, bypassing the need for lab optimization that has historically bottlenecked enzyme development. While prior protein design software yielded enzymes with only trace levels of activity, RFdiffusion2 delivers catalytic proficiencies — a rigorous measure of how well an enzyme captures and converts a mole

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A new study led by the Baker Lab shows for the first time that functional, full-length antibodies can be developed entirely on computers — no animal immunizations or extensive screening required. This breakthrough could reshape the $200 billion antibody drug industry and open pathways to treatments for diseases currently considered undruggable. The work builds on a preprint we posted in March 2024 showing AI could ge

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SEATTLE — In celebration of Nobel Prize announcement week, Fay Lin, PhD, Senior Editor, Technology at GEN, sat down with Nobel Laureate, David Baker, PhD, to unpack the hype vs. reality surrounding designing proteins from scratch (de novo) and explore what’s needed for the AI revolution to truly transform medicine. Baker, who is also the director of the Institute for Protein Design (IPD) at the University of Washingt

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Pranam Chatterjee Designs Novel AI Frameworks for Biotechnology In the News / September 18, 2025 When he enrolled at Dartmouth, Pranam Chatterjee, Assistant Professor and Africk-Lesley Distinguished Scholar of Innovation in Engineering with a joint appointment in Bioengineering (BE) and in Computer and Information Science (CIS), did not start off as an engineering student. He majored in religion. “I had a deep desire

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Sam Rodriques, an alum of the Synthetic Neurobiology research group, has been recognized in the Innovators category of the TIME100 AI 2025 list. Dr. Rodriques co-founded the nonprofit FutureHouse in 2023, with the aim of giving scientists new tools to break through bottlenecks and improve their workflows. To that end, FutureHouse is developing AI agents that can formulate hypotheses, conduct experiments, and analyze

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A theoretical physicist by training, Sam Rodriques recognized the possible uses of AI for scientific discovery. In 2023 he co-founded the nonprofit FutureHouse, with a goal to build AI scientists that can formulate hypotheses, conduct experiments, and analyze data by themselves. While AI science is an increasingly crowded field, FutureHouse’s audacious vision and scientific bonafides have earned it the backing of Eri

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