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Drug discovery is a multi-stage process that comprises two costly majorsteps: pre-clinical research and clinical trials. Among its stages, leadoptimization easily consumes more than half of the pre-clinical budget. Wepropose a combined machine learning and molecular modeling approach thatautomates lead optimization workflow \textit{in silico}. The initial datacollection is achieved with physics-based molecular dynamics (MD) simulation.Contact matrices are calculated as the preliminary features extracted from thesimulations. To take advantage of the temporal information from thesimulations, we enhanced contact matrices data with temporal dynamismrepresentation, which are then modeled with unsupervised convolutionalvariational autoencoder (CVAE). Finally, conventional clustering method andCVAE-based clustering method are compared with metrics to rank the submolecularstructures and propose potential candidates for lead optimization. With no needfor extensive structure-activity relationship database, our method provides newhints for drug modification hotspots which can be used to improve drugefficacy. Our workflow can potentially reduce the lead optimization turnaroundtime from months/years to days compared with the conventional labor-intensiveprocess and thus can potentially become a valuable tool for medicalresearchers.

CASTELO: Clustered Atom Subtypes aidEd Lead Optimization -- a combined machine learning and molecular modeling method