AI is a viable alternative to high throughput screening: a 318-target study
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...
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| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/179577 |
| _version_ | 1824456209336369152 |
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| author | Wallach, Izhar Bernard, Denzil Nguyen, Kong Ho, Gregory Morrison, Adrian Stecula, Adrian Rosnik, Andreana O’Sullivan, Ann Marie Davtyan, Aram Samudio, Ben Thomas, Bill Worley, Brad Butler, Brittany Laggner, Christian Thayer, Desiree Moharreri, Ehsan Friedland, Greg Truong, Ha van den Bedem, Henry Ng, Ho Leung Stafford, Kate Sarangapani, Krishna Giesler, Kyle Ngo, Lien Mysinger, Michael Ahmed, Mostafa Anthis, Nicholas J. Henriksen, Niel Gniewek, Pawel Eckert, Sam de Oliveira, Saulo Suterwala, Shabbir PrasadPrasad, Srimukh Veccham Krishna Shek, Stefani Contreras, Stephanie Hare, Stephanie Palazzo, Teresa O’Brien, Terrence E. Van Grack, Tessa Williams, Tiffany Chern, Ting-Rong Kenyon, Victor Lee, Andreia H. Cann, Andrew B. Bergman, Bastiaan Anderson, Brandon M. Cox, Bryan D. Warrington, Jeffrey M. Sorenson, Jon M. Goldenberg, Joshua M. Young, Matthew A. DeHaan, Nicholas Pemberton, Ryan P. Schroedl, Stefan Abramyan, Tigran M. Gupta, Tushita Mysore, Venkatesh Presser, Adam G. Ferrando, Adolfo A. Andricopulo, Adriano D. Ghosh, Agnidipta Ayachi, Aicha Gharbi Mushtaq, Aisha Shaqra, Ala M. Toh, Alan Kie Leong Smrcka, Alan V. Ciccia, Alberto de Oliveira, Aldo Sena Sverzhinsky, Aleksandr de Sousa, Alessandra Mara Agoulnik, Alexander I. Kushnir, Alexander Freiberg, Alexander N. Statsyuk, Alexander V. Gingras, Alexandre R. Degterev, Alexei Tomilov, Alexey Vrielink, Alice Garaeva, Alisa A. Bryant-Friedrich, Amanda Caflisch, Amedeo Patel, Amit K. Rangarajan, Amith Vikram Matheeussen, An Battistoni, Andrea Caporali, Andrea Chini, Andrea Ilari, Andrea Mattevi, Andrea Foote, Andrea Talbot Trabocchi, Andrea Stahl, Andreas Herr, Andrew B. Berti, Andrew Freywald, Andrew Reidenbach, Andrew G. Lam, Andrew Cuddihy, Andrew R. White, Andrew Taglialatela, Angelo |
| author2 | School of Biological Sciences |
| author_facet | School of Biological Sciences Wallach, Izhar Bernard, Denzil Nguyen, Kong Ho, Gregory Morrison, Adrian Stecula, Adrian Rosnik, Andreana O’Sullivan, Ann Marie Davtyan, Aram Samudio, Ben Thomas, Bill Worley, Brad Butler, Brittany Laggner, Christian Thayer, Desiree Moharreri, Ehsan Friedland, Greg Truong, Ha van den Bedem, Henry Ng, Ho Leung Stafford, Kate Sarangapani, Krishna Giesler, Kyle Ngo, Lien Mysinger, Michael Ahmed, Mostafa Anthis, Nicholas J. Henriksen, Niel Gniewek, Pawel Eckert, Sam de Oliveira, Saulo Suterwala, Shabbir PrasadPrasad, Srimukh Veccham Krishna Shek, Stefani Contreras, Stephanie Hare, Stephanie Palazzo, Teresa O’Brien, Terrence E. Van Grack, Tessa Williams, Tiffany Chern, Ting-Rong Kenyon, Victor Lee, Andreia H. Cann, Andrew B. Bergman, Bastiaan Anderson, Brandon M. Cox, Bryan D. Warrington, Jeffrey M. Sorenson, Jon M. Goldenberg, Joshua M. Young, Matthew A. DeHaan, Nicholas Pemberton, Ryan P. Schroedl, Stefan Abramyan, Tigran M. Gupta, Tushita Mysore, Venkatesh Presser, Adam G. Ferrando, Adolfo A. Andricopulo, Adriano D. Ghosh, Agnidipta Ayachi, Aicha Gharbi Mushtaq, Aisha Shaqra, Ala M. Toh, Alan Kie Leong Smrcka, Alan V. Ciccia, Alberto de Oliveira, Aldo Sena Sverzhinsky, Aleksandr de Sousa, Alessandra Mara Agoulnik, Alexander I. Kushnir, Alexander Freiberg, Alexander N. Statsyuk, Alexander V. Gingras, Alexandre R. Degterev, Alexei Tomilov, Alexey Vrielink, Alice Garaeva, Alisa A. Bryant-Friedrich, Amanda Caflisch, Amedeo Patel, Amit K. Rangarajan, Amith Vikram Matheeussen, An Battistoni, Andrea Caporali, Andrea Chini, Andrea Ilari, Andrea Mattevi, Andrea Foote, Andrea Talbot Trabocchi, Andrea Stahl, Andreas Herr, Andrew B. Berti, Andrew Freywald, Andrew Reidenbach, Andrew G. Lam, Andrew Cuddihy, Andrew R. White, Andrew Taglialatela, Angelo |
| author_sort | Wallach, Izhar |
| collection | NTU |
| description | 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. |
| first_indexed | 2025-02-19T03:50:28Z |
| format | Journal Article |
| id | ntu-10356/179577 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2025-02-19T03:50:28Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1795772024-08-12T15:31:59Z AI is a viable alternative to high throughput screening: a 318-target study Wallach, Izhar Bernard, Denzil Nguyen, Kong Ho, Gregory Morrison, Adrian Stecula, Adrian Rosnik, Andreana O’Sullivan, Ann Marie Davtyan, Aram Samudio, Ben Thomas, Bill Worley, Brad Butler, Brittany Laggner, Christian Thayer, Desiree Moharreri, Ehsan Friedland, Greg Truong, Ha van den Bedem, Henry Ng, Ho Leung Stafford, Kate Sarangapani, Krishna Giesler, Kyle Ngo, Lien Mysinger, Michael Ahmed, Mostafa Anthis, Nicholas J. Henriksen, Niel Gniewek, Pawel Eckert, Sam de Oliveira, Saulo Suterwala, Shabbir PrasadPrasad, Srimukh Veccham Krishna Shek, Stefani Contreras, Stephanie Hare, Stephanie Palazzo, Teresa O’Brien, Terrence E. Van Grack, Tessa Williams, Tiffany Chern, Ting-Rong Kenyon, Victor Lee, Andreia H. Cann, Andrew B. Bergman, Bastiaan Anderson, Brandon M. Cox, Bryan D. Warrington, Jeffrey M. Sorenson, Jon M. Goldenberg, Joshua M. Young, Matthew A. DeHaan, Nicholas Pemberton, Ryan P. Schroedl, Stefan Abramyan, Tigran M. Gupta, Tushita Mysore, Venkatesh Presser, Adam G. Ferrando, Adolfo A. Andricopulo, Adriano D. Ghosh, Agnidipta Ayachi, Aicha Gharbi Mushtaq, Aisha Shaqra, Ala M. Toh, Alan Kie Leong Smrcka, Alan V. Ciccia, Alberto de Oliveira, Aldo Sena Sverzhinsky, Aleksandr de Sousa, Alessandra Mara Agoulnik, Alexander I. Kushnir, Alexander Freiberg, Alexander N. Statsyuk, Alexander V. Gingras, Alexandre R. Degterev, Alexei Tomilov, Alexey Vrielink, Alice Garaeva, Alisa A. Bryant-Friedrich, Amanda Caflisch, Amedeo Patel, Amit K. Rangarajan, Amith Vikram Matheeussen, An Battistoni, Andrea Caporali, Andrea Chini, Andrea Ilari, Andrea Mattevi, Andrea Foote, Andrea Talbot Trabocchi, Andrea Stahl, Andreas Herr, Andrew B. Berti, Andrew Freywald, Andrew Reidenbach, Andrew G. Lam, Andrew Cuddihy, Andrew R. White, Andrew Taglialatela, Angelo School of Biological Sciences Medicine, Health and Life Sciences High throughput screening AI 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. Published version 2024-08-12T01:58:44Z 2024-08-12T01:58:44Z 2024 Journal Article Wallach, I., Bernard, D., Nguyen, K., Ho, G., Morrison, A., Stecula, A., Rosnik, A., O’Sullivan, A. M., Davtyan, A., Samudio, B., Thomas, B., Worley, B., Butler, B., Laggner, C., Thayer, D., Moharreri, E., Friedland, G., Truong, H., van den Bedem, H., ...Taglialatela, A. (2024). AI is a viable alternative to high throughput screening: a 318-target study. Scientific Reports, 14(1), 7526-. https://dx.doi.org/10.1038/s41598-024-54655-z 2045-2322 https://hdl.handle.net/10356/179577 10.1038/s41598-024-54655-z 38565852 2-s2.0-85191821387 1 14 7526 en Scientific Reports © 2024 The Author(s). 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 http://creativecommons.org/licenses/by/4.0/. application/pdf |
| spellingShingle | Medicine, Health and Life Sciences High throughput screening AI Wallach, Izhar Bernard, Denzil Nguyen, Kong Ho, Gregory Morrison, Adrian Stecula, Adrian Rosnik, Andreana O’Sullivan, Ann Marie Davtyan, Aram Samudio, Ben Thomas, Bill Worley, Brad Butler, Brittany Laggner, Christian Thayer, Desiree Moharreri, Ehsan Friedland, Greg Truong, Ha van den Bedem, Henry Ng, Ho Leung Stafford, Kate Sarangapani, Krishna Giesler, Kyle Ngo, Lien Mysinger, Michael Ahmed, Mostafa Anthis, Nicholas J. Henriksen, Niel Gniewek, Pawel Eckert, Sam de Oliveira, Saulo Suterwala, Shabbir PrasadPrasad, Srimukh Veccham Krishna Shek, Stefani Contreras, Stephanie Hare, Stephanie Palazzo, Teresa O’Brien, Terrence E. Van Grack, Tessa Williams, Tiffany Chern, Ting-Rong Kenyon, Victor Lee, Andreia H. Cann, Andrew B. Bergman, Bastiaan Anderson, Brandon M. Cox, Bryan D. Warrington, Jeffrey M. Sorenson, Jon M. Goldenberg, Joshua M. Young, Matthew A. DeHaan, Nicholas Pemberton, Ryan P. Schroedl, Stefan Abramyan, Tigran M. Gupta, Tushita Mysore, Venkatesh Presser, Adam G. Ferrando, Adolfo A. Andricopulo, Adriano D. Ghosh, Agnidipta Ayachi, Aicha Gharbi Mushtaq, Aisha Shaqra, Ala M. Toh, Alan Kie Leong Smrcka, Alan V. Ciccia, Alberto de Oliveira, Aldo Sena Sverzhinsky, Aleksandr de Sousa, Alessandra Mara Agoulnik, Alexander I. Kushnir, Alexander Freiberg, Alexander N. Statsyuk, Alexander V. Gingras, Alexandre R. Degterev, Alexei Tomilov, Alexey Vrielink, Alice Garaeva, Alisa A. Bryant-Friedrich, Amanda Caflisch, Amedeo Patel, Amit K. Rangarajan, Amith Vikram Matheeussen, An Battistoni, Andrea Caporali, Andrea Chini, Andrea Ilari, Andrea Mattevi, Andrea Foote, Andrea Talbot Trabocchi, Andrea Stahl, Andreas Herr, Andrew B. Berti, Andrew Freywald, Andrew Reidenbach, Andrew G. Lam, Andrew Cuddihy, Andrew R. White, Andrew Taglialatela, Angelo AI is a viable alternative to high throughput screening: a 318-target study |
| title | AI is a viable alternative to high throughput screening: a 318-target study |
| title_full | AI is a viable alternative to high throughput screening: a 318-target study |
| title_fullStr | AI is a viable alternative to high throughput screening: a 318-target study |
| title_full_unstemmed | AI is a viable alternative to high throughput screening: a 318-target study |
| title_short | AI is a viable alternative to high throughput screening: a 318-target study |
| title_sort | ai is a viable alternative to high throughput screening a 318 target study |
| topic | Medicine, Health and Life Sciences High throughput screening AI |
| url | https://hdl.handle.net/10356/179577 |
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