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Researchers at Stanford have developed ComBind, a computational platform that improves prediction of how drug molecules bind to their protein targets by combining structural modeling with readily available binding data from other molecules.

Researchers at Stanford and WashU developed cannabinoid receptor agonists with distribution mainly to peripheral tissues. Cannabinoid agonists are relevant for treatment of many indications, but current clinical use is limited by a variety of side effects.

A new deep-learning system called Atomic Rotationally Equivariant Scorer (ARES) significantly improves the prediction of RNA structures over previous artificial intelligence (AI) models.

Stanford researchers have developed a geometric deep learning based novel method to aid in identification and discovery of novel drug scaffolds as well as to optimize known scaffolds, as a means to combat the major challenge in drug discovery.