Predicting Strategies for Lead Optimization via Learning to Rank

Abstract

: Lead optimization is an essential step in drug discovery in which the chemical structures of compounds are modified to improve characteristics such as binding a ffi nity, target selectivity, physicochemical properties, and tox-icity. We present a concept for a computational compound optimization system that outputs optimized compounds from hit compounds by using previous lead optimization data from a pharmaceutical company. In this study, to predict the drug-likeness of compounds in the evaluation function of this system, we evaluated and compared the ability to correctly predict lead optimization strategies through learning to rank methods.