用药效团条件控制生成3D分子，高效设计新药
Pharmacophore-Conditioned Diffusion Model for Ligand-Based De Novo Drug Design
- 将药效团信息融入扩散模型，指导3D分子生成
- 生成分子匹配药效团约束能力优于现有方法
- 无需蛋白结构即可获得高对接分数，适合新靶点
新药研发中，针对缺乏结构或功能数据的新靶点，设计有效分子仍是耗时且成本高昂的挑战。药效团建模可捕捉分子与靶点结合所需的关键特征。本文提出PharmaDiff，一种基于药效团条件的3D分子生成扩散模型。该模型采用基于Transformer的架构，将原子级3D药效团表示融入生成过程，实现对预设药效团假设精准匹配的3D分子图生成。在多组测试中，PharmaDiff在满足3D药效团约束方面表现优于现有基于配体的药物设计方法，并在多种蛋白质上实现了更高的对接分数，且无需目标蛋白结构。通过融合药效团建模与3D生成技术，PharmaDiff为理性药物设计提供了强大而灵活的框架。
原文摘要 · Abstract (English)
Developing bioactive molecules remains a central, time- and cost-heavy challenge in drug discovery, particularly for novel targets lacking structural or functional data. Pharmacophore modeling presents an alternative for capturing the key features required for molecular bioactivity against a biological target. In this work, we present PharmaDiff, a pharmacophore-conditioned diffusion model for 3D molecular generation. PharmaDiff employs a transformer-based architecture to integrate an atom-based representation of the 3D pharmacophore into the generative process, enabling the precise generation of 3D molecular graphs that align with predefined pharmacophore hypotheses. Through comprehensive testing, PharmaDiff demonstrates superior performance in matching 3D pharmacophore constraints compared to ligand-based drug design methods. Additionally, it achieves higher docking scores across a range of proteins in structure-based drug design, without the need for target protein structures. By integrating pharmacophore modeling with 3D generative techniques, PharmaDiff offers a powerful and flexible framework for rational drug design.
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