Latent Chemical Space Searching for Plug-in Multi-objective Molecule Generation
<|reference_start|>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.<|reference_end|>
@article{liu2024latent,
    title={Latent Chemical Space Searching for Plug-in Multi-objective Molecule
  Generation},
    author={Ningfeng Liu (1 and 2), Jie Yu (1), Siyu Xiu (1), Xinfang Zhao (1),
  Siyu Lin (1), Bo Qiang (1), Ruqiu Zheng (1), Hongwei Jin (1), Liangren Zhang
  (1), Zhenming Liu (1 and 3) ((1) State Key Laboratory of Natural and
  Biomimetic Drugs, School of Pharmaceutical Sciences, Peking University, (2)
  Peking-Tsinghua Center for Life Science (CLS), Peking University, (3) State
  Key Laboratory of Pharmaceutical Biotechnology, Nanjing University)},
    journal={arXiv preprint arXiv:2404.06691},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.06691},
    primaryClass={q-bio.BM cs.LG cs.NE}
}