Résumé
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.