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01Nature Portfolio · Nature
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 consortium of pharmaceutical companies reports that using such data to train AI models of protein folding improves model performance markedly. The group used OpenFold3 — an open-source replication of AlphaFold 3 — to develop a new model trained on more than 20,000 proprietary protein structures. The system outperformed both comparable ones trained on public data alone and those trained on the siloed datasets of individual firms. The study, described in a blog post, has not been peer-reviewed, and the model is not publicly available. “You add all this data, and you get a pretty big bump in performance,” says Mohammed AlQuraishi, a computational biologist at Columbia University in New York City, who was part of the effort. AlphaFold is running out of data — so drug firms are building their own version The findings, he says, strengthen the case for generating similar publicly available datasets to supercharge protein-folding AIs. One such project, called OpenBind and supported by up to £8 million (US$10.8 million) in UK government funding, released hundreds of new protein structures last month, with thousands more in the works. An untapped vein The Protein Data Bank (PDB), an open repository of more than 200,000 experimentally determined protein structures, was the bedrock of AlphaFold 2’s training data. It enabled the tool to predict protein structures with startling accuracy — a breakthrough recognized with the 2024 Nobel Prize in Chemistry. The model’s successors, including AlphaFold 3, added the ability to predict how proteins will interact with other molecules, including potential drugs. But the PDB has relatively few examples of experimentally determined structures interacting with drug-like molecules — maybe just 10,000, says Paul Mortenson, vice-president for computational chemistry and informatics at Astex Pharmaceuticals in Cambridge, UK. That lack of data is a problem for drug-discovery efforts. Research has suggested that the accuracy of Alph…
Nature Portfolio · Nature Biotechnology
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 identification of a diverse set of analytes, including 21 proteinogenic amino acids, three post-translationally modified (PTM) amino acids, four canonical nucleoside monophosphates (NMPs), three epigenetically modified NMPs, four monosaccharides and five peptides. Combined with machine learning, this sensor achieves an overall accuracy of 98.7%. Furthermore, it generates predictable event signatures for specific analyte types, enabling the machine learning model to identify analytes without strictly relying on prearchived event features, as demonstrated by the nanopore analysis of yeast cell extract. Lastly, we apply MspA-FPBA for compositional analysis of G2 glycopeptides. Future integration of hydrolase with MspA-FPBA may further improve MspA-FPBA utility as a single system to identify diverse biomolecules. Modified nanopore simultaneously identifies diverse biomolecules and their modifications.
Nature Portfolio · Scientific Data
Permafrost thaw accelerates the initiation and expansion of retrogressive thaw slumps (RTSs), with critical implications for the terrestrial carbon cycle and land-surface energy budgets. However, long-term datasets characterizing spatiotemporal variations in RTS susceptibility across Northern Hemisphere permafrost regions remain lacking. Here, we integrate 55,673 georeferenced RTS occurrence records with machine learning models to generate a 1-km spatiotemporal dataset of RTS susceptibility from 2000 to 2100 under multiple climate change scenarios. RTS susceptibility is defined as the relative occurrence probability of RTS under specified environmental conditions. We independently modelled RTS susceptibility for the Arctic and Tibetan Plateau and merged the individual outputs to construct a spatially harmonized dataset for the Northern Hemisphere. Model performance was evaluated using standard classification metrics, yielding mean area under the receiver operating characteristic curve (AUC) values of 0.96 ± 0.005. The dataset includes georeferenced RTS inventories and gridded susceptibility maps for the entire Northern Hemisphere permafrost domain, covering the baseline period and diverse future climate scenarios. These products provide a foundational resource for quantifying permafrost hazard risks, infrastructure exposure, and carbon-climate feedbacks under climate warming.
Discovery
Machine learning, at its core, is about generalization, not memorization. You hand your learning algorithm a pile of training examples and use them to fit a model. But the goal is not to perform well on the training examples — that's easy, you could just memorize the answers. The goal is to perform well on new examples that you have never before seen. If a model does well on the data it was trained on but poorly on fresh data, it hasn’t actually learned anything; you have only fooled yourself into thinking it has. This failure mode has a name: overfitting. Anyone who has taken an introductory statistics or machine learning class knows the standard defense. You hold out some of your data and refuse to train on it. In practice, this held-out data plays two roles. A validation set is one you consult repeatedly while building the model — to compare candidates, tune hyperparameters, and decide what to try next. A final test set (or holdout) is meant to be touched only once, at the very end: because the training procedure never saw it, strong performance there is a correct proxy for the new examples you will encounter in the wild. Machine learning, at its core, is about generalization, not memorization The “holdout” condition is crucial, though. The correct-proxy guarantee holds if the held-out set stays genuinely unseen. If you check your performance on it, tweak your training procedure in response, recheck, and iterate, chasing better and better numbers, that set is no longer unseen; it has become part of your training procedure. Do this enough times, and you can overfit it just as you might have overfit the training set, and you have lost your proxy for unseen data. This is true of any held-out set you reuse this way, including a validation set, which is reused by design. A puzzle at the heart of machine learning Real machine learning research looks exactly like the iterative improvement loop we just described. Everyone gauges performance using a handful of benchmark datasets that go unrevised for years. The research community repeats an enormous, distributed loop: evaluate a model on the benchmark, revise the training procedure, re-evaluate, publish, and let the next group eke out a little more improvement. This is precisely the kind of hill-climbing against a held-out set that, by the textbook account, ought to produce rampant overfitting. By now, the leader…
PhAI Labs
PhAI Labs opened a scientist collaboration program focused first on life science and biomedicine, inviting collaborators with scientific questions, data, computational or experimental environments, and real validation conditions.
