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.

本研究通过开发一种多功能的 “插件” 分子生成模型，结合多个目标相关性、药物相似性和可合成性，来解决多目标生成、模型适应性以及药物发现实际应用中的挑战。在药物发现的背景下，我们改进了粒子群优化算法（PSO），通过比较实验发现 PSO-ENP 是多目标分子生成和优化的最佳变体。该模型还整合了一种新颖的目标配体亲和力预测器，通过支持三维信息和改善合成可行性来提高模型的效用。实例研究聚焦于生成和优化类似药物的大型海洋天然产物，突显了 PSO-ENP 的有效性，并展示了其在实际药物发现应用中的巨大潜力。