AI-Augmented R-Group Exploration in Medicinal Chemistry.

Journal: Journal of chemical information and modeling
PMID: 39959996

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 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.

Authors

  • Hongtao Zhao
    State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China.
  • Karolina KwapieĊ„
    Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg 43183, Sweden.
  • Eva Nittinger
    Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, 43183 Gothenburg, Sweden.
  • Christian Tyrchan
    Medicinal Chemistry, Early RIA, Biopharmaceuticals R&D, AstraZeneca, Pepparedsleden 1, Gothenburg 43183, Sweden.
  • Magnus Nilsson
    Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg 43183, Sweden.
  • Susanne Berglund
    Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg 43183, Sweden.
  • Werngard Czechtizky
    Medicinal Chemistry, Research and Early Development, Respiratory and Immunology (R&I), BioPharmaceuticals R&D, AstraZeneca, Gothenburg 43183, Sweden.

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