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Extracting medicinal chemistry intuition via preference machine learning.

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.

SUBMITTER: Choung OH

PROVIDER: S-EPMC10618272 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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Extracting medicinal chemistry intuition via preference machine learning.

Choung Oh-Hyeon OH Vianello Riccardo R Segler Marwin M Stiefl Nikolaus N Jiménez-Luna José J

Nature communications 20231031 1

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 f ...[more]

PMID: 37907461

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