面向目标蛋白结合的分子生成：分层一致性扩散模型
Molecule Generation for Target Protein Binding with Hierarchical Consistency Diffusion Model
AI总结：
针对3D分子生成在有效性和可靠性上的不足，提出分层扩散模型AMDiff，整合原子与基序多视图联合训练并引入结合位点条件，显著提升了针对目标蛋白口袋的分子生成有效性与新颖性，加速药物发现。
摘要：
生成与目标蛋白结合的分子结构（即新化学实体）对于药物发现中的先导化合物识别与优化至关重要。尽管用于3D分子生成的基于原子和基序的深度学习模型取得了进展，但当前方法在有效性和可靠性方面常面临困难。为解决这些问题，我们开发了Atom-Motif Consistency Diffusion Model (AMDiff)，利用联合训练范式进行多视图学习。该模型采用分层扩散架构，整合了分子的原子级和基序级视图，从而能够全面探索互补信息。通过利用无分类器引导并将结合位点特征作为条件输入，AMDiff确保了跨不同目标的稳健分子生成。与现有方法相比，AMDiff在生成适合各种蛋白口袋的分子方面展现出更优的有效性和新颖性。以蛋白激酶为靶点的案例研究，包括Anaplastic Lymphoma Kinase (ALK)和Cyclin-dependent kinase 4 (CDK4)，证明了该模型在基于结构的从头药物设计中的能力。总体而言，AMDiff弥合了原子视图与基序视图药物发现之间的差距，并加速了目标感知分子生成的过程。
英文摘要：
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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