Generative Active Learning for the Search of Small-molecule Protein Binders
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arXiv
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| Natura: | Preprint |
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2024
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| author | Korablyov, Maksym Liu, Cheng-Hao Jain, Moksh van der Sloot, Almer M. Jolicoeur, Eric Ruediger, Edward Nica, Andrei Cristian Bengio, Emmanuel Lapchevskyi, Kostiantyn St-Cyr, Daniel Schuetz, Doris Alexandra Butoi, Victor Ion Rector-Brooks, Jarrid Blackburn, Simon Feng, Leo Nekoei, Hadi Gottipati, SaiKrishna Vijayan, Priyesh Gupta, Prateek Rampášek, Ladislav Avancha, Sasikanth Bacon, Pierre-Luc Hamilton, William L. Paige, Brooks Misra, Sanchit Jastrzebski, Stanislaw Kamil Kaul, Bharat Precup, Doina Hernández-Lobato, José Miguel Segler, Marwin Bronstein, Michael Marinier, Anne Tyers, Mike Bengio, Yoshua |
| author_facet | Korablyov, Maksym Liu, Cheng-Hao Jain, Moksh van der Sloot, Almer M. Jolicoeur, Eric Ruediger, Edward Nica, Andrei Cristian Bengio, Emmanuel Lapchevskyi, Kostiantyn St-Cyr, Daniel Schuetz, Doris Alexandra Butoi, Victor Ion Rector-Brooks, Jarrid Blackburn, Simon Feng, Leo Nekoei, Hadi Gottipati, SaiKrishna Vijayan, Priyesh Gupta, Prateek Rampášek, Ladislav Avancha, Sasikanth Bacon, Pierre-Luc Hamilton, William L. Paige, Brooks Misra, Sanchit Jastrzebski, Stanislaw Kamil Kaul, Bharat Precup, Doina Hernández-Lobato, José Miguel Segler, Marwin Bronstein, Michael Marinier, Anne Tyers, Mike Bengio, Yoshua |
| contents | Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01616 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Generative Active Learning for the Search of Small-molecule Protein Binders Korablyov, Maksym Liu, Cheng-Hao Jain, Moksh van der Sloot, Almer M. Jolicoeur, Eric Ruediger, Edward Nica, Andrei Cristian Bengio, Emmanuel Lapchevskyi, Kostiantyn St-Cyr, Daniel Schuetz, Doris Alexandra Butoi, Victor Ion Rector-Brooks, Jarrid Blackburn, Simon Feng, Leo Nekoei, Hadi Gottipati, SaiKrishna Vijayan, Priyesh Gupta, Prateek Rampášek, Ladislav Avancha, Sasikanth Bacon, Pierre-Luc Hamilton, William L. Paige, Brooks Misra, Sanchit Jastrzebski, Stanislaw Kamil Kaul, Bharat Precup, Doina Hernández-Lobato, José Miguel Segler, Marwin Bronstein, Michael Marinier, Anne Tyers, Mike Bengio, Yoshua Biomolecules Artificial Intelligence Machine Learning Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exhibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH. |
| title | Generative Active Learning for the Search of Small-molecule Protein Binders |
| topic | Biomolecules Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2405.01616 |