Machine learning features for ligand-based drug discovery.

Stanford researchers have developed descriptors based on OpenEye Rapid Overlay of Chemical Structures (ROCS) that, when paired with machine learning methods improve virtual screening performance. Current practices of hit identification in drug discovery is often expensive and require high-throughput screens of large compound collections. However, computational shortcuts can help prioritize experiments and reduce screening costs by leveraging the structure of either the target or set of active compounds. This approach uses 3D shape and chemical similarity, which are natural descriptors for ligand-based approaches.