A data science roadmap for open science organizations engaged in early-stage drug discovery
Artikel i vetenskaplig tidskrift, 2024
The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.
Författare
Kristina Edfeldt
Karolinska universitetssjukhuset
Aled M. Edwards
University of Toronto
Ola Engkvist
Chalmers, Data- och informationsteknik
Judith Günther
Bayer AG
Matthew Hartley
European Bioinformatics Institute
David G. Hulcoop
Open Targets
European Bioinformatics Institute
Andrew R. Leach
European Bioinformatics Institute
Brian D. Marsden
University of Oxford
Amelie Menge
Johann Wolfgang Goethe Universität Frankfurt am Main
Leonie Misquitta
National Library of Medicine (NLM)
Susanne Müller
Johann Wolfgang Goethe Universität Frankfurt am Main
Dafydd R. Owen
Pfizer
Kristof T. Schütt
Pfizer
Nicholas Skelton
Genentech
Andreas Steffen
Pfizer
Alexander Tropsha
University of Northern Colorado
Erik Vernet
Novo Nordisk
Yanli Wang
National Library of Medicine (NLM)
James Wellnitz
University of Northern Colorado
Timothy M. Willson
University of Northern Colorado
Djork Arné Clevert
Pfizer
Benjamin Haibe-Kains
Vector Institute for AI
University Health Network
University of Toronto
Lovisa Holmberg Schiavone
AstraZeneca AB
Matthieu Schapira
University of Toronto
Nature Communications
2041-1723 (ISSN) 20411723 (eISSN)
Vol. 15 1 5640Ämneskategorier (SSIF 2025)
Bioinformatik (beräkningsbiologi)
Datavetenskap (datalogi)
DOI
10.1038/s41467-024-49777-x