Nittinger, E. | Search bioRxiv

bioRxiv2024

AI-Augmented R-Group Exploration in Medicinal Chemistry

Efficient R-group exploration in the vast chemical space, enabled by increasingly available building blocks or generative AI, remains an open challenge. Here, we developed an enhanced Free-Wilson QSAR model embedding R-groups by atom-centric pharmacophoric features. Regioisomers of R-groups can be distinguished by explicitly accounting for the atomic positions. Good predictivity is observed consistently across 12 public datasets. Integrated into an open-source program, we showcase its application in performing classic Free-Wilson analysis as well as R-group exploration in uncharted chemical space.

bioinformatics↗

bioRxiv2024

Enhanced Thompson Sampling by Roulette Wheel Selection for Screening Ultra-Large Combinatorial Libraries

Chemical space exploration has gained significant interest with the increase in available building blocks, which enables the creation of ultra-large virtual libraries containing billions or even trillions of compounds. However, the challenge of selecting most suitable compounds for synthesis arises, and one such challenge is hit expansion. Recently, Thompson sampling, a probabilistic search approach, has been proposed by Walters et al. to achieve efficiency gains by operating in the reagent space rather than the product space. Here, we aim to address some of its shortcomings and propose optimizations. We introduce a warmup routine to ensure that initial probabilities are set for all reagents with a minimum number of molecules evaluated. Additionally, a roulette wheel selection is proposed with adapted stop criteria to improve sampling efficiency, and belief distributions of reagents are only updated when they appear in new molecules. We demonstrate that a 100% recovery rate can be achieved by sampling 0.1% of the fully enumerated library, showcasing the effectiveness of our proposed optimizations.

bioinformatics↗