用分层扩散模型生成能精准结合靶蛋白的分子，提升药物设计效率。
Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion Model
- 分层扩散架构融合原子与基团视角，联合训练提升生成质量。
- 在ALK和CDK4等蛋白上生成分子的有效性与新颖性显著优于现有方法。
- 适合需要结构导向新药设计的研究者快速探索候选分子。
高效生成可结合靶蛋白的新分子结构是药物发现中先导化合物识别与优化的关键。尽管原子级和基团级深度学习模型在三维分子生成方面取得进展，但当前方法常面临生成有效性与可靠性不足的问题。为此，我们提出原子-基团一致性扩散模型（AMDiff），采用多视图联合训练范式。该模型具有分层扩散架构，整合分子的原子级与基团级信息，实现互补信息的全面探索。通过无分类器引导并引入结合位点特征作为条件输入，AMDiff能在多种靶标上实现稳健的分子生成。相比现有方法，AMDiff在生成适配不同蛋白口袋的分子时表现出更优的有效性与新颖性。针对蛋白激酶（包括ALK和CDK4）的案例研究展示了其在基于结构的从头药物设计中的能力。总体而言，AMDiff弥合了原子视角与基团视角在药物发现中的差距，加速了靶向分子生成过程。
原文摘要 · Abstract (English)
Effective generation of molecular structures, or new chemical entities, that bind to target proteins is crucial for lead identification and optimization in drug discovery. Despite advancements in atom- and motif-wise deep learning models for 3D molecular generation, current methods often struggle with validity and reliability. To address these issues, we develop the Atom-Motif Consistency Diffusion Model (AMDiff), utilizing a joint-training paradigm for multi-view learning. This model features a hierarchical diffusion architecture that integrates both atom- and motif-level views of molecules, allowing for comprehensive exploration of complementary information. By leveraging classifier-free guidance and incorporating binding site features as conditional inputs, AMDiff ensures robust molecule generation across diverse targets. Compared to existing approaches, AMDiff exhibits superior validity and novelty in generating molecules tailored to fit various protein pockets. Case studies targeting protein kinases, including Anaplastic Lymphoma Kinase (ALK) and Cyclin-dependent kinase 4 (CDK4), demonstrate the model's capability in structure-based de novo drug design. Overall, AMDiff bridges the gap between atom-view and motif-view drug discovery and speeds up the process of target-aware molecular generation.
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