Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation

• URL: http://arxiv.org/abs/2404.06691v1
• Date: Wed, 10 Apr 2024 02:37:24 GMT
• Title: Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
• Authors: Ningfeng Liu, Jie Yu, Siyu Xiu, Xinfang Zhao, Siyu Lin, Bo Qiang, Ruqiu Zheng, Hongwei Jin, Liangren Zhang, Zhenming Liu,
• Abstract summary: We develop a versatile 'plug-in' molecular generation model that incorporates objectives related to target affinity, drug-likeness, and synthesizability. We identify PSO-ENP as the optimal variant for multi-objective molecular generation and optimization.
• Score: 9.442146563809953
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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