Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
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..
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Preprint| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
arXiv.org - (2024) vom: 09. Apr. Zur Gesamtaufnahme - year:2024 |
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| Sprache: |
Englisch |
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| Beteiligte Personen: |
Liu, Ningfeng [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
000 |
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| PPN (Katalog-ID): |
XAR043222013 |
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| 520 | |a 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. | ||
| 650 | 4 | |a Quantitative Biology - Biomolecules |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Machine Learning |7 (dpeaa)DE-84 | |
| 650 | 4 | |a Computer Science - Neural and Evolutionary Computing |7 (dpeaa)DE-84 | |
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| 650 | 4 | |a 000 |7 (dpeaa)DE-84 | |
| 700 | 1 | |a Yu, Jie |e verfasserin |4 aut | |
| 700 | 1 | |a Xiu, Siyu |e verfasserin |4 aut | |
| 700 | 1 | |a Zhao, Xinfang |e verfasserin |4 aut | |
| 700 | 1 | |a Lin, Siyu |e verfasserin |4 aut | |
| 700 | 1 | |a Qiang, Bo |e verfasserin |4 aut | |
| 700 | 1 | |a Zheng, Ruqiu |e verfasserin |4 aut | |
| 700 | 1 | |a Jin, Hongwei |e verfasserin |4 aut | |
| 700 | 1 | |a Zhang, Liangren |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Zhenming |e verfasserin |4 aut | |
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