Li Guanlue, Jiang Chenran, Gao Ziqi, Liu Yu, Liu Chenyang, Chen Jiean, Huang Yong, Li Jia
Data Science and Analytics, The Hong Kong University of Science and Technology (Guang Zhou) Guangzhou 511400 China
Pingshan Translational Medicine Center, Shenzhen Bay Laboratory Shenzhen 518118 China
Chem Sci. 2025 Sep 2. doi: 10.1039/d5sc02113h.
Effective generation of molecular structures that bind to target proteins is crucial for lead identification and optimization in drug discovery. Despite advancements in atom- and motif-wise 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-views of molecules, allowing for comprehensive exploration of complementary information. By leveraging classifier-free guidance and incorporating topological 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 capability in structure-based drug design. Overall, AMDiff bridges the gap between atom-view and motif-view drug discovery and accelerating the development of target-specific molecules.
生成与靶蛋白结合的分子结构对于药物发现中的先导物识别和优化至关重要。尽管在用于三维分子生成的基于原子和基序的模型方面取得了进展，但当前方法在有效性和可靠性方面常常面临困难。为了解决这些问题，我们开发了原子-基序一致性扩散模型（AMDiff），采用联合训练范式进行多视图学习。该模型具有分层扩散架构，整合了分子的原子视图和基序视图，允许对互补信息进行全面探索。通过利用无分类器引导并将拓扑特征作为条件输入，AMDiff确保在不同靶标上稳健地生成分子。与现有方法相比，AMDiff在生成适合各种蛋白质口袋的分子方面表现出卓越的有效性和新颖性。针对包括间变性淋巴瘤激酶（ALK）和细胞周期蛋白依赖性激酶4（CDK4）在内的蛋白激酶的案例研究证明了其在基于结构的药物设计中的能力。总体而言，AMDiff弥合了原子视图和基序视图药物发现之间的差距，并加速了靶向特异性分子的开发。
