  Drug discovery is a multi-stage process that comprises two costly major
steps: pre-clinical research and clinical trials. Among its stages, lead
optimization easily consumes more than half of the pre-clinical budget. We
propose a combined machine learning and molecular modeling approach that
automates lead optimization workflow \textit{in silico}. The initial data
collection is achieved with physics-based molecular dynamics (MD) simulation.
Contact matrices are calculated as the preliminary features extracted from the
simulations. To take advantage of the temporal information from the
simulations, we enhanced contact matrices data with temporal dynamism
representation, which are then modeled with unsupervised convolutional
variational autoencoder (CVAE). Finally, conventional clustering method and
CVAE-based clustering method are compared with metrics to rank the submolecular
structures and propose potential candidates for lead optimization. With no need
for extensive structure-activity relationship database, our method provides new
hints for drug modification hotspots which can be used to improve drug
efficacy. Our workflow can potentially reduce the lead optimization turnaround
time from months/years to days compared with the conventional labor-intensive
process and thus can potentially become a valuable tool for medical
researchers.
