Tool to isolate and identify impactful submolecular motifs – rBRICS

Abstract

Molecular fragmentation has been frequently used for machine learning, molecular modeling, and drug discovery studies. However, the current molecular fragmentation tools often lead to large fragments that are useful to limited tasks. Specifically, long aliphatic chains, certain connected ring structures, fused rings, as well as various nitrogen-containing molecular entities often remain intact when using BRICS. With no known methods to solve this issue, we find that the fragments taken from BRICS are inflexible for tasks such as fragment-based machine learning, coarse-graining, and ligand-protein interaction assessment. In this work, we develop a revised BRICS (r-BRICS) module that allows more flexible fragmentation on a wider variety of molecules. We show that r-BRICS generates smaller fragments than BRICS, allowing localized fragment assessments. Furthermore, r-BRICS generates a fragment database with significantly more unique small fragments than BRICS, which is useful for fragment-based drug discovery, submolecular motif identification and coarse-grained simulations.