A University of Toronto team has published a new method, HIP, in Nature Computational Science that directly predicts a molecule's full Hessian matrix, skipping the costly differentiation step.
An active-learning loop combined with geometric deep learning improves reaction prediction for drug chemistry when data is scarce, as AI learns to judge where a molecule should break.
The "2-step" method makes liquid chromatography retention-time prediction for small molecules work out of the box, with no retraining for new systems, a direct benefit to drug discovery and natural-product research.
Researchers at Columbia University (Cornish lab and Ken Shepard lab) used an AI design-build-test cycle to engineer yeast that senses PFOA and PFOS, and built a dip-in test strip that reads PFAS levels in water instantly.
XtalPi disclosed details of its Kodexia platform on October 6, in which generative AI, biological modeling and automated experiments form a closed loop to speed up the discovery of small interfering RNA (siRNA) therapies.
A New York University team reports in Chemical Science an AI trained to predict hydrogen atom positions in drug molecules from stability-related chemical patterns, tackling the long-standing problem of tautomer assignment.
AI drug discovery company Transcripta Bio and "AI chemistry foundry" Synfini report obtaining a validation-grade lead compound against the MSH3 gene in a matter of weeks, versus an industry benchmark of 12-18 months from hit to candidate nomination.