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bioRxiv · 10.1101/2024.09.23.614417

AI-Augmented R-Group Exploration in Medicinal Chemistry

Zhao, H.·Kwapien, K.·Nittinger, E.·Tyrchan, C.·Nilsson, M.·Berglund, S.·Czechtizky, W.
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Abstract

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.

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Zhao, H., Kwapien, K., Nittinger, E., Tyrchan, C., Nilsson, M., Berglund, S., Czechtizky, W.. 2024-09-24. AI-Augmented R-Group Exploration in Medicinal Chemistry. https://doi.org/10.1101/2024.09.23.614417

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