Molecular optimization, aimed at improving binding affinity or other molecular properties, is a crucial task in drug discovery that often relies on the expertise of medicinal chemists. Recently, deep learning-based 3D generative models showed promise in enhancing the efficiency of molecular optimization. However, these models often struggle to adequately consider binding affinities with protein targets during lead optimization. Herein, we propose a 3D pocket-aware and affinity-guided diffusion model, named Diffleop, to optimize molecules with enhanced binding affinity. The model explicitly incorporates the knowledge of protein-ligand binding affinity to guide the denoising sampling for molecule generation with high affinity. The comprehensive evaluations indicated that Diffleop outperforms baseline models across multiple metrics, especially in terms of binding affinity.

本研究解决了现有深度学习三维生成模型在引导优化过程中未能充分考虑与蛋白靶标结合亲和力的问题。提出的Diffleop模型通过明确结合蛋白-配体亲和力的知识，引导分子生成，提高了亲和力的优化效果。综合评估结果表明，Diffleop在多个指标上优于基线模型，特别是在结合亲和力方面。

一种三维口袋感知和亲和力引导的扩散模型用于引导优化