Extracting medicinal chemistry intuition via preference machine learning

Oh-Hyeon Choung, Riccardo Vianello, Marwin Segler, Nikolaus Stiefl () and José Jiménez-Luna ()
Additional contact information
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

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: 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.

Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.nature.com/articles/s41467-023-42242-1 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-42242-1

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-023-42242-1

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().