Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
Fuente:
arXiv
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| Autori principali: | , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
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| _version_ | 1866929308564455424 |
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| author | Liu, Ningfeng Yu, Jie Xiu, Siyu Zhao, Xinfang Lin, Siyu Qiang, Bo Zheng, Ruqiu Jin, Hongwei Zhang, Liangren Liu, Zhenming |
| author_facet | Liu, Ningfeng Yu, Jie Xiu, Siyu Zhao, Xinfang Lin, Siyu Qiang, Bo Zheng, Ruqiu Jin, Hongwei Zhang, Liangren Liu, Zhenming |
| contents | Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology. However, challenges remain in multi-objective generation, model adaptability, and practical application in drug discovery. In this study, we developed a versatile 'plug-in' molecular generation model that incorporates multiple objectives related to target affinity, drug-likeness, and synthesizability, facilitating its application in various drug development contexts. We improved the Particle Swarm Optimization (PSO) in the context of drug discoveries, and identified PSO-ENP as the optimal variant for multi-objective molecular generation and optimization through comparative experiments. The model also incorporates a novel target-ligand affinity predictor, enhancing the model's utility by supporting three-dimensional information and improving synthetic feasibility. Case studies focused on generating and optimizing drug-like big marine natural products were performed, underscoring PSO-ENP's effectiveness and demonstrating its considerable potential for practical drug discovery applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_06691 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation Liu, Ningfeng Yu, Jie Xiu, Siyu Zhao, Xinfang Lin, Siyu Qiang, Bo Zheng, Ruqiu Jin, Hongwei Zhang, Liangren Liu, Zhenming Biomolecules Machine Learning Neural and Evolutionary Computing Molecular generation, an essential method for identifying new drug structures, has been supported by advancements in machine learning and computational technology. However, challenges remain in multi-objective generation, model adaptability, and practical application in drug discovery. In this study, we developed a versatile 'plug-in' molecular generation model that incorporates multiple objectives related to target affinity, drug-likeness, and synthesizability, facilitating its application in various drug development contexts. We improved the Particle Swarm Optimization (PSO) in the context of drug discoveries, and identified PSO-ENP as the optimal variant for multi-objective molecular generation and optimization through comparative experiments. The model also incorporates a novel target-ligand affinity predictor, enhancing the model's utility by supporting three-dimensional information and improving synthetic feasibility. Case studies focused on generating and optimizing drug-like big marine natural products were performed, underscoring PSO-ENP's effectiveness and demonstrating its considerable potential for practical drug discovery applications. |
| title | Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation |
| topic | Biomolecules Machine Learning Neural and Evolutionary Computing |
| url | https://arxiv.org/abs/2404.06691 |