Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design
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arXiv
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| Main Authors: | , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2021
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| _version_ | 1866909067205672960 |
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| author | Li, Jie Zhang, Oufan Wang, Yingze Sun, Kunyang Guan, Xingyi Bagni, Dorian Haghighatlari, Mojtaba Kearns, Fiona L. Parks, Conor Amaro, Rommie E. Head-Gordon, Teresa |
| author_facet | Li, Jie Zhang, Oufan Wang, Yingze Sun, Kunyang Guan, Xingyi Bagni, Dorian Haghighatlari, Mojtaba Kearns, Fiona L. Parks, Conor Amaro, Rommie E. Head-Gordon, Teresa |
| contents | The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with real-time 3D molecular docking using AutoDock Vina, thereby simultaneously creating chemical novelty while constraining molecules for shape and molecular compatibility with target active sites. Moreover, through the use of various types of reward functions, we can generate new molecules that are chemically similar to a target ligand, which can be grown from known protein bound fragments, as well as to create molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflow that filters out Pan-assay interference compounds, Lipinski rule violations, and poor synthetic accessibility, with options for cross-validation against other docking scoring functions and automation of a molecular dynamics simulation to measure pose stability. Because our approach only relies on the structure of the target protein, iMiner can be easily adapted for future development of other inhibitors or small molecule therapeutics of any target protein. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2110_01806 |
| institution | arXiv |
| publishDate | 2021 |
| record_format | arxiv |
| spellingShingle | Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design Li, Jie Zhang, Oufan Wang, Yingze Sun, Kunyang Guan, Xingyi Bagni, Dorian Haghighatlari, Mojtaba Kearns, Fiona L. Parks, Conor Amaro, Rommie E. Head-Gordon, Teresa Biomolecules Biological Physics Chemical Physics Data Analysis, Statistics and Probability The viability of a new drug molecule is a time and resource intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibitor molecules for target proteins by combining deep reinforcement learning with real-time 3D molecular docking using AutoDock Vina, thereby simultaneously creating chemical novelty while constraining molecules for shape and molecular compatibility with target active sites. Moreover, through the use of various types of reward functions, we can generate new molecules that are chemically similar to a target ligand, which can be grown from known protein bound fragments, as well as to create molecules that enforce interactions with target residues in the protein active site. The iMiner algorithm is embedded in a composite workflow that filters out Pan-assay interference compounds, Lipinski rule violations, and poor synthetic accessibility, with options for cross-validation against other docking scoring functions and automation of a molecular dynamics simulation to measure pose stability. Because our approach only relies on the structure of the target protein, iMiner can be easily adapted for future development of other inhibitors or small molecule therapeutics of any target protein. |
| title | Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design |
| topic | Biomolecules Biological Physics Chemical Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2110.01806 |