用三维口袋感知与亲和力引导生成新分子，提升药物优化效率
A 3D pocket-aware and affinity-guided diffusion model for lead optimization
- 基于扩散模型，显式融合蛋白-配体结合亲和力知识
- 在多个指标上超越基线模型，尤其显著提升结合亲和力
- 适合药物发现中需精准优化亲和力的研究者使用
分子优化旨在改善分子的结合亲和力或其他性质，是药物发现中的关键任务，通常依赖药物化学家的专业知识。近年来，基于深度学习的3D生成模型在提升分子优化效率方面展现出潜力。然而，这些模型在先导化合物优化过程中往往难以充分考虑与蛋白靶标的结合亲和力。本文提出一种3D口袋感知且亲和力引导的扩散模型Diffleop，用于生成具有更高结合亲和力的分子。该模型显式引入蛋白-配体结合亲和力知识，指导去噪采样过程以生成高亲和力分子。综合评估表明，Diffleop在多个指标上优于基线模型，尤其是在结合亲和力方面表现突出。
原文摘要 · Abstract (English)
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.
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