Zhou Zhong-Xing, Zhang Hong-Xing, Zheng Qingchuan
School of Pharmaceutical Sciences, Jilin University, Changchun, 130023, China.
Institute of Theoretical Chemistry, College of Chemistry, Jilin University, Changchun, 130023, China.
J Pharm Anal. 2025 Jun;15(6):101229. doi: 10.1016/j.jpha.2025.101229. Epub 2025 Feb 13.
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) mutations are influenced by random and uncontrollable factors, and the risk of the next widespread epidemic remains. Dual-target drugs that synergistically act on two targets exhibit strong therapeutic effects and advantages against mutations. In this study, a novel computational workflow was developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main protease selected as the two target proteins. The drug-like molecules of our self-constructed 3D scaffold database were used as high-throughput molecular docking probes for feature extraction of two target protein pockets. A multi-layer perceptron (MLP) was employed to embed the binding affinities into a latent space as conditional vectors to control conditional distribution. Utilizing a conditional generative neural network, cG-SchNet, with 3D Euclidean group (E3) symmetries, the conditional probability distributions of molecular 3D structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated. The 1D probability, 2D joint probability, and 2D cumulative probability distribution results indicate that the generated sets are significantly enhanced compared to the training set in the high binding affinity area. Among the 201 generated molecules, 42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol, demonstrating structure diversity along with strong dual-target affinities, good absorption, distribution, metabolism, excretion, and toxicity (ADMET) properties, and ease of synthesis. Dual-target drugs are rare and difficult to find, and our "high-throughput docking-multi-conditional generation" workflow offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.
严重急性呼吸综合征冠状病毒2（SARS-CoV-2）突变受随机且不可控因素影响，下一次大流行的风险依然存在。协同作用于两个靶点的双靶点药物对突变表现出强大的治疗效果和优势。在本研究中，开发了一种新型计算工作流程，以包膜蛋白和主要蛋白酶作为两个靶蛋白来设计双靶点SARS-CoV-2候选抑制剂。我们自建的3D支架数据库中的类药物分子被用作高通量分子对接探针，用于两个靶蛋白口袋的特征提取。采用多层感知器（MLP）将结合亲和力嵌入潜在空间作为条件向量以控制条件分布。利用具有3D欧几里得群（E3）对称性的条件生成神经网络cG-SchNet，获取分子3D结构的条件概率分布，并生成了一组新型SARS-CoV-2双靶点候选抑制剂。一维概率、二维联合概率和二维累积概率分布结果表明，在高结合亲和力区域，生成的集合与训练集相比有显著增强。在生成的201个分子中，42个分子的总结合亲和力超过17.0 kcal/mol，其中9个分子的总结合亲和力超过19.0 kcal/mol，显示出结构多样性以及强大的双靶点亲和力、良好的吸收、分布、代谢、排泄和毒性（ADMET）特性，且易于合成。双靶点药物稀少且难以找到，我们的“高通量对接-多条件生成”工作流程为设计或优化有效的双靶点SARS-CoV-2抑制剂提供了广泛的选择。
