Enhanced utility of AI/ML methods during lead optimization by inclusion of 3D ligand information - Bleicher, Leo S.; van Daelen, Ton; Honeycutt, J. Dana; Hassan, Moises; Chandrasekhar, Jayaraman; Shirley, William; Tsui, Vickie; Schmitz, Uli (Frontiers in Drug Discovery, 2022)
AI/ML methods in drug discovery are maturing and their utility and impact is likely to permeate many aspects of drug discovery including lead finding and lead optimization. Typical methods utilize ML-models for structure-property prediction with simple 2D-based chemical representations of the small molecules. Further, limited data, especially pertaining to novel targets, make it difficult to build effective structure-activity ML-models. Here we describe our recent work using the BIOVIA Generative Therapeutics Design (GTD) application, which is equipped to take advantage of 3D structural models of ligand protein interaction, i.e., pharmacophoric representation of desired features. Using an SAR data set pertaining to the discovery of SYK inhibitors entospletinib and lanraplenib in addition to two unrelated clinical SYK inhibitors, we show how several common problems in lead finding and lead optimization can be effectively addressed with GTD. This includes an effort to retrospectively re-identify drug candidate molecules based on data from an intermediate stage of the project using chemical space constraints and the application of evolutionary pressure within GTD. Additionally, studies of how the GTD platform can be configured to generate molecules incorporating features from multiple unrelated molecule series show how the GTD methods apply AI/ML to drug discovery.