insitro Validates AI-Enabled POSH Platform in Nature Communications, Bridging Critical Gap in Drug Discovery

Summary

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 Communications validating its POSH (Pooled Optical Screening in Human cells) platform. The study details the development of CellPaint-POSH, a scalable approach that integrates pooled CRISPR screening, high-content imaging, and advanced machine learning to map gene function at scale without relying on predefined biomarkers or human-engineered hypotheses. Across multiple large-scale experiments, the study demonstrates that biologically meaningful gene networks emerge directly from morphology-based readouts and AI-derived embeddings. By capturing the subtle, holistic changes in cells rather than isolated metrics, the platform reveals cellular relationships and therapeutic targets that conventional, hypothesis-driven analyses systematically miss. “Biology doesn’t organize itself according to the features we’ve learned to measure,” said Daphne Koller, Ph.D., founder and CEO of insitro. “insitro’s POSH platform has demonstrated that self-supervised models trained on unbiased cellular morphology can reconstruct gene function and causal relationships without being told what to look for. We’re finally able to interrogate the genome at scale while preserving the phenotypic complexity where disease mechanisms actually live.” Drug discovery has historically been constrained by a fundamental compromise: researchers were forced to choose between screening thousands of genes using low-resolution metrics (scale) or interrogating a handful of genes with deep biological insight (depth). Prioritizing scale typically meant relying on coarse, low-dimensional readouts — such as simple cell viability or single-protein expression — that inherently obscure complex biology. These “looking under the streetlight” approaches often miss the subtle, multi-factorial changes that drive disease. Conversely, while high-content phenotypes offer rich insights, generating and interpreting them at scale has been logistically prohibitive, making it impractical to map the functional ar…