A data science roadmap for open science organizations engaged in early-stage drug discovery

• Published in: Nature communications Vol. 15; no. 1; pp. 5640 - 10
• Main Authors: Edfeldt, Kristina, Edwards, Aled M., Engkvist, Ola, Günther, Judith, Hartley, Matthew, Hulcoop, David G., Leach, Andrew R., Marsden, Brian D., Menge, Amelie, Misquitta, Leonie, Müller, Susanne, Owen, Dafydd R., Schütt, Kristof T., Skelton, Nicholas, Steffen, Andreas, Tropsha, Alexander, Vernet, Erik, Wang, Yanli, Wellnitz, James, Willson, Timothy M., Clevert, Djork-Arné, Haibe-Kains, Benjamin, Schiavone, Lovisa Holmberg, Schapira, Matthieu
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 05-07-2024; Nature Publishing Group; Nature Portfolio
• Subjects: 639/638/309/2144; 706/648/697; Artificial Intelligence; Automation; Cloud Computing; Data integration; Data management; Data mining; Data Mining - methods; Data processing; Data science; Data Science - methods; Databases, Factual; Design of experiments; Drug development; Drug discovery; Drug Discovery - methods; Experimental design; Humanities and Social Sciences; Humans; Information Dissemination - methods; Learning algorithms; Machine Learning; Modelling; multidisciplinary; Open access; Perspective; R&D; Real time; Research & development; Robustness; Science; Science (multidisciplinary); Time integration
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-024-49777-x

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.
Artificial intelligence is greatly accelerating research in drug discovery, but its development is still hindered by the lack of available data. Here the authors present data management and data science recommendations to help reach AI’s potential in the field.