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Extracting medicinal chemistry intuition via preference machine learning

Author

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  • Oh-Hyeon Choung

    (Novartis Institutes for Biomedical Research)

  • Riccardo Vianello

    (Novartis Institutes for Biomedical Research)

  • Marwin Segler

    (Microsoft Research AI4Science)

  • Nikolaus Stiefl

    (Novartis Institutes for Biomedical Research)

  • José Jiménez-Luna

    (Microsoft Research AI4Science)

Abstract

The lead optimization process in drug discovery campaigns is an arduous endeavour where the input of many medicinal chemists is weighed in order to reach a desired molecular property profile. Building the expertise to successfully drive such projects collaboratively is a very time-consuming process that typically spans many years within a chemist’s career. In this work we aim to replicate this process by applying artificial intelligence learning-to-rank techniques on feedback that was obtained from 35 chemists at Novartis over the course of several months. We exemplify the usefulness of the learned proxies in routine tasks such as compound prioritization, motif rationalization, and biased de novo drug design. Annotated response data is provided, and developed models and code made available through a permissive open-source license.

Suggested Citation

  • Oh-Hyeon Choung & Riccardo Vianello & Marwin Segler & Nikolaus Stiefl & José Jiménez-Luna, 2023. "Extracting medicinal chemistry intuition via preference machine learning," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42242-1
    DOI: 10.1038/s41467-023-42242-1
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