Learning on topological surface and geometric structure for 3D molecular generation
Learning on topological surface and geometric structure for 3D molecular generation
复制标题
DOI：
10.1038/s43588-023-00530-2
复制
发表时间：
2023-10
期刊：
影响因子：
--
通讯作者：
Odin Zhang;Tianyue Wang;Gaoqi Weng;Dejun Jiang;Ning Wang;Xiaorui Wang;Huifeng Zhao;Jialun Wu-Jia
中科院分区：
文献类型：
--
作者：
Odin Zhang;Tianyue Wang;Gaoqi Weng;Dejun Jiang;Ning Wang;Xiaorui Wang;Huifeng Zhao;Jialun Wu-Jia
高效的从头设计是计算机辅助药物发现的一大挑战。近年来，实用的结构特定的三维分子生成已经开始出现，但大多数方法将目标结构作为条件输入来偏向分子生成，并且没有充分了解控制分子构象和结合络合物稳定性的详细原子相互作用。这些细节的遗漏导致许多模型难以为各种治疗靶点输出合理的分子。在这里，为了应对这一挑战，我们设计了一个名为SurfGen的模型，它以一种非常类似于比喻的钥匙和锁原理的方式设计分子。SurfGen由两个等变神经网络组成，它们分别捕捉口袋表面上的拓扑相互作用和配体原子与表面节点之间的空间相互作用。SurfGen在许多基准测试中表现优于其他方法，其对口袋结构的高度敏感性使基于生成模型的有效解决方案能够解决突变导致的耐药性这一棘手问题。
Highly effective de novo design is a grand challenge of computer-aided drug discovery. Practical structure-specific three-dimensional molecule generations have started to emerge in recent years, but most approaches treat the target structure as a conditional input to bias the molecule generation and do not fully learn the detailed atomic interactions that govern the molecular conformation and stability of the binding complexes. The omission of these fine details leads to many models having difficulty in outputting reasonable molecules for a variety of therapeutic targets. Here, to address this challenge, we formulate a model, called SurfGen, that designs molecules in a fashion closely resembling the figurative key-and-lock principle. SurfGen comprises two equivariant neural networks, Geodesic-GNN and Geoatom-GNN, which capture the topological interactions on the pocket surface and the spatial interaction between ligand atoms and surface nodes, respectively. SurfGen outperforms other methods in a number of benchmarks, and its high sensitivity on the pocket structures enables an effective generative-model-based solution to the thorny issue of mutation-induced drug resistance.