Human-level molecular optimization driven by mol-gene evolution
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866910208303824896 |
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| author | Fang, Jiebin Mao, Churu Zhu, Yuchen Chen, Xiaoming Hsieh, Chang-Yu Ma, Zhongjun |
| author_facet | Fang, Jiebin Mao, Churu Zhu, Yuchen Chen, Xiaoming Hsieh, Chang-Yu Ma, Zhongjun |
| contents | De novo molecule generation allows the search for more drug-like hits across a vast chemical space. However, lead optimization is still required, and the process of optimizing molecular structures faces the challenge of balancing structural novelty with pharmacological properties. This study introduces the Deep Genetic Molecular Modification Algorithm (DGMM), which brings structure modification to the level of medicinal chemists. A discrete variational autoencoder (D-VAE) is used in DGMM to encode molecules as quantization code, mol-gene, which incorporates deep learning into genetic algorithms for flexible structural optimization. The mol-gene allows for the discovery of pharmacologically similar but structurally distinct compounds, and reveals the trade-offs of structural optimization in drug discovery. We demonstrate the effectiveness of the DGMM in several applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12910 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Human-level molecular optimization driven by mol-gene evolution Fang, Jiebin Mao, Churu Zhu, Yuchen Chen, Xiaoming Hsieh, Chang-Yu Ma, Zhongjun Machine Learning Artificial Intelligence Neural and Evolutionary Computing Chemical Physics Biomolecules De novo molecule generation allows the search for more drug-like hits across a vast chemical space. However, lead optimization is still required, and the process of optimizing molecular structures faces the challenge of balancing structural novelty with pharmacological properties. This study introduces the Deep Genetic Molecular Modification Algorithm (DGMM), which brings structure modification to the level of medicinal chemists. A discrete variational autoencoder (D-VAE) is used in DGMM to encode molecules as quantization code, mol-gene, which incorporates deep learning into genetic algorithms for flexible structural optimization. The mol-gene allows for the discovery of pharmacologically similar but structurally distinct compounds, and reveals the trade-offs of structural optimization in drug discovery. We demonstrate the effectiveness of the DGMM in several applications. |
| title | Human-level molecular optimization driven by mol-gene evolution |
| topic | Machine Learning Artificial Intelligence Neural and Evolutionary Computing Chemical Physics Biomolecules |
| url | https://arxiv.org/abs/2406.12910 |