LS-MolGen: Ligand-and-Structure Dual-driven Deep Reinforcement Learning for Target-specific Molecular Generation Improves Binding Affinity and Novelty
Research Square (Research Square)(2023)
摘要
Abstract Molecule generative models based on deep learning have attracted significant attention in de novo drug design. However, most current generative approaches are either only ligand-based or only structure-based, which do not leverage the complementary knowledge from ligands and the structure of binding target. In this work, we proposed a new ligand and structure combined molecular generative model, LS-MolGen, that integrates representation learning, transfer learning, and reinforcement learning. Focus knowledge from transfer learning and special explore strategy in reinforcement learning enables LS-MolGen to generate novel and active molecules efficiently. The results of evaluation using EGFR and case study of inhibitor design for SARS-CoV-2 Mpro showed that LS-MolGen outperformed other state-of-the-art ligand-based or structure-based generative models and was capable of de novo designing promising compounds with novel scaffold and high binding affinity. Thus, we recommend that this proof-of-concept ligand-and-structure-based generative model will provide a promising new tool for target-specific molecular generation and drug design.
更多查看译文
关键词
deep reinforcement learning,molecular,ls-molgen,ligand-and-structure,dual-driven,target-specific
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要