AI-Augmented R-Group Exploration in Medicinal Chemistry

被引：0

作者：

Zhao, Hongtao ^{[1
]}

Kwapien, Karolina ^{[1
]}

Nittinger, Eva ^{[1
]}

Tyrchan, Christian ^{[1
]}

Nilsson, Magnus ^{[1
]}

Berglund, Susanne ^{[1
]}

Czechtizky, Werngard ^{[1
]}

机构：

[1] AstraZeneca, Med Chem Res & Early Dev, Resp & Immunol R&I, BioPharmaceut R&D, S-43183 Gothenburg, Sweden

来源：

JOURNAL OF CHEMICAL INFORMATION AND MODELING | 2025年 / 65卷 / 05期

关键词：

MATCHED MOLECULAR PAIRS;

D O I：

10.1021/acs.jcim.4c02326

中图分类号：

R914 [药物化学];

学科分类号：

100701 ;

摘要：

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 data sets. Integrated into an open-source program, we showcase its application in performing Free-Wilson analysis as well as R-group exploration in an uncharted chemical space.

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收藏

页码：2251 / 2255

页数：5

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