BB-SAR: An Application for Data-driven Analysis and Rational Design of Medicinal Chemistry Series.

Abstract

In drug discovery, medicinal chemists face the challenge of generating and analyzing large data sets, often exceeding a thousand molecules and numerous physicochemical and biological properties. To address this, we introduced BB-SAR, an interpolative methodology that tackles both data complexity and interpretability, by breaking down molecules into their constituent building blocks (BBs). Establishing a direct correlation between molecules and their constituent BBs enables the association of these BBs with their respective biological and physicochemical properties. This facilitates more intuitive data analysis and enables the identification of critical trends between molecular features and their associated properties. While individual BBs rarely dictate property behavior, their combinations do. BB-SAR identifies impactful combinations for designing new, improved compounds. Additionally, it simplifies traditional medicinal chemistry analysis strategies and enhances the efficiency of drug discovery by providing a more inherent understanding of complex data sets within a concise framework.