Please use this identifier to cite or link to this item:
http://bura.brunel.ac.uk/handle/2438/29093
Title: | AI is a viable alternative to high throughput screening: a 318-target study |
Authors: | Wallach, I Bernard, D Nguyen, K Ho, G Morrison, A Stecula, A Rosnik, A O’Sullivan, AM Davtyan, A Samudio, B Thomas, B Tomilov, A Vrielink, A Garaeva, AA Bryant-Friedrich, A Caflisch, A Patel, AK Rangarajan, AV Matheeussen, A Battistoni, A Caporali, A Chini, A Ilari, A Mattevi, A Foote, AT Trabocchi, A Stahl, A Herr, AB Berti, A Freywald, A Reidenbach, AG Lam, A Cuddihy, AR White, A Taglialatela, A Gadar, K McCarthy, RR Worley, B Butler, B Laggner, C Thayer, D Moharreri, E Friedland, G Truong, H van den Bedem, H Ng, HL Stafford, K Sarangapani, K Giesler, K Ngo, L Mysinger, M Ahmed, M Anthis, NJ Henriksen, N Gniewek, P Eckert, S de Oliveira, S Suterwala, S PrasadPrasad, SVK Shek, S Contreras, S Hare, S Palazzo, T O’Brien, TE Van Grack, T Williams, T Chern, TR Kenyon, V Lee, AH Cann, AB Bergman, B Anderson, BM Cox, BD Warrington, JM Sorenson, JM Goldenberg, JM Young, MA DeHaan, N Pemberton, RP Schroedl, S Abramyan, TM Gupta, T Mysore, V Presser, AG Ferrando, AA Andricopulo, AD Ghosh, A Ayachi, AG Mushtaq, A Shaqra, AM Toh, AKL Smrcka, AV Ciccia, A de Oliveira, AS Sverzhinsky, A de Sousa, AM Agoulnik, AI Kushnir, A Freiberg, AN Statsyuk, AV Gingras, AR Degterev, A |
Keywords: | drug discovery;high-throughput screening;machine learning;virtual screening |
Issue Date: | 2-Apr-2024 |
Publisher: | Springer Nature |
Citation: | The Atomwise AIMS Program (2024) 'AI is a viable alternative to high throughput screening: a 318-target study', Scientific Reports, 14 (1), 7526, pp. 1 - 16. doi: 10.1038/s41598-024-54655-z. |
Abstract: | High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery. |
Description: | Data availability:
All data generated or analyzed during this study are included in this published article and its supplementary information files. Supplementary Information is available online at: https://www.nature.com/articles/s41598-024-54655-z#Sec15 . Correction to: Scientific Reports https://doi.org/10.1038/s41598-024-54655-z, published online 02 April 2024 The original version of this Article contained errors. The corrections are available online: The Atomwise AIMS Program. Author Correction: AI is a viable alternative to high throughput screening: a 318-target study. Sci Rep 14, 21579 (2024). DOI URL: https://doi.org/10.1038/s41598-024-70321-w (they are also available on the Corrigendum PDF file, below). . |
URI: | https://bura.brunel.ac.uk/handle/2438/29093 |
DOI: | https://doi.org/10.1038/s41598-024-54655-z |
Other Identifiers: | ORCiD: Ronan R McCarthy https://orcid.org/0000-0002-7480-6352 7526 |
Appears in Collections: | Dept of Life Sciences Research Papers |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
FullText.pdf | Copyright © The Author(s) 2024. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. | 1.58 MB | Adobe PDF | View/Open |
Corrigendum.pdf | Copyright © The Author(s) 2024. Rights and permissions: Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/. | 901.38 kB | Adobe PDF | View/Open |
This item is licensed under a Creative Commons License