Gilead Sciences-Employing 3D in AI and ML-based Virtual Compound Optimization
AI/ML methods in drug discovery are maturing and their utility and impact is likely to permeate many aspects of drug discovery. Many such methods, however, utilize SAR data without explicit use of 3D structural information of the ligand protein complex. Here we describe our recent work using the BIOVIA Generative Therapeutics Design (GTD) application, which can take advantage of 3D structural models, i.e. pharmacophoric representation of ligand protein interaction as well as typical docking/scoring steps.
Using Gilead’s SYK inihibitor data set we show how several common types of problems in medicinal chemistry can be effectively addressed via GTD. This includes an effort to retrospectively re-identify drug candidate molecules based on data from an intermediate stage of the project which illustrates the use of chemical space constraints and the application of evolutionary pressure within GTD. Additionally, a study of how the GTD system can blend features from alternate chemical series shows some of the unique ways GTD applies AI/ML to drug discovery. 2022 BIOVIA Conference
