Large-scale, granular biological datasets such as proteomic, metabolomic, and transcriptomic profiles are powering the current shift toward precision medicine.
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.
Stanford researchers have developed a novel representation learning model that improves data-driven learning by incorporating more relevant data relationships. This approach significantly enhances both model performance and inference accuracy.
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.