Latent Chemical Space Searching for Plug-in Multi-objective Molecule
  Generation
Ningfeng Liu Jie Yu Siyu Xiu Xinfang Zhao Siyu Lin Bo Qiang Ruqiu Zheng Hongwei Jin Liangren Zhang Zhenming Liu
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.