Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework
As an appealing approach for discovering novel leads, the key advantage of de novo drug design lies in its ability to explore a much broader dimension of chemical space, without being confined to the knowledge of existing compounds. So far, many generative models have been described in the literatur...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-12-01
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| Series: | Molecules |
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| Online Access: | https://www.mdpi.com/1420-3049/30/1/18 |
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| author | Xiangying Zhang Haotian Gao Yifei Qi Yan Li Renxiao Wang |
| author_facet | Xiangying Zhang Haotian Gao Yifei Qi Yan Li Renxiao Wang |
| author_sort | Xiangying Zhang |
| collection | DOAJ |
| description | As an appealing approach for discovering novel leads, the key advantage of de novo drug design lies in its ability to explore a much broader dimension of chemical space, without being confined to the knowledge of existing compounds. So far, many generative models have been described in the literature, which have completely redefined the concept of de novo drug design. However, many of them lack practical value for real-world drug discovery. In this work, we have developed a graph-based generative model within a reinforcement learning framework, namely, METEOR (Molecular Exploration Through multiplE-Objective Reinforcement). The backend agent of METEOR is based on the well-established GCPN model. To ensure the overall quality of the generated molecular graphs, we implemented a set of rules to identify and exclude undesired substructures. Importantly, METEOR is designed to conduct multi-objective optimization, i.e., simultaneously optimizing binding affinity, drug-likeness, and synthetic accessibility of the generated molecules under the guidance of a special reward function. We demonstrate in a specific test case that without prior knowledge of true binders to the chosen target protein, METEOR generated molecules with superior properties compared to those in the ZINC 250k data set. In conclusion, we have demonstrated the potential of METEOR as a practical tool for generating rational drug-like molecules in the early phase of drug discovery. |
| format | Article |
| id | doaj-art-b24b7cefab3f488aad01b09f951ac64f |
| institution | Kabale University |
| issn | 1420-3049 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Molecules |
| spelling | doaj-art-b24b7cefab3f488aad01b09f951ac64f2025-01-10T13:18:37ZengMDPI AGMolecules1420-30492024-12-013011810.3390/molecules30010018Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning FrameworkXiangying Zhang0Haotian Gao1Yifei Qi2Yan Li3Renxiao Wang4Department of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, ChinaDepartment of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, ChinaDepartment of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, ChinaDepartment of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, ChinaDepartment of Medicinal Chemistry, School of Pharmacy, Fudan University, 826 Zhangheng Road, Shanghai 201203, ChinaAs an appealing approach for discovering novel leads, the key advantage of de novo drug design lies in its ability to explore a much broader dimension of chemical space, without being confined to the knowledge of existing compounds. So far, many generative models have been described in the literature, which have completely redefined the concept of de novo drug design. However, many of them lack practical value for real-world drug discovery. In this work, we have developed a graph-based generative model within a reinforcement learning framework, namely, METEOR (Molecular Exploration Through multiplE-Objective Reinforcement). The backend agent of METEOR is based on the well-established GCPN model. To ensure the overall quality of the generated molecular graphs, we implemented a set of rules to identify and exclude undesired substructures. Importantly, METEOR is designed to conduct multi-objective optimization, i.e., simultaneously optimizing binding affinity, drug-likeness, and synthetic accessibility of the generated molecules under the guidance of a special reward function. We demonstrate in a specific test case that without prior knowledge of true binders to the chosen target protein, METEOR generated molecules with superior properties compared to those in the ZINC 250k data set. In conclusion, we have demonstrated the potential of METEOR as a practical tool for generating rational drug-like molecules in the early phase of drug discovery.https://www.mdpi.com/1420-3049/30/1/18molecular generative modelde novo drug designmulti-objective optimizationGCPN |
| spellingShingle | Xiangying Zhang Haotian Gao Yifei Qi Yan Li Renxiao Wang Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework Molecules molecular generative model de novo drug design multi-objective optimization GCPN |
| title | Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework |
| title_full | Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework |
| title_fullStr | Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework |
| title_full_unstemmed | Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework |
| title_short | Generation of Rational Drug-like Molecular Structures Through a Multiple-Objective Reinforcement Learning Framework |
| title_sort | generation of rational drug like molecular structures through a multiple objective reinforcement learning framework |
| topic | molecular generative model de novo drug design multi-objective optimization GCPN |
| url | https://www.mdpi.com/1420-3049/30/1/18 |
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