  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.
