Generation of SARS-CoV-2 dual-target candidate inhibitors through 3D equivariant conditional generative neural networks
Zhong-Xing Zhou, Hong‐Xing Zhang, Qing‐Chuan Zheng · Journal of Pharmaceutical Analysis · 2025
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. The design of “High-Throughput Docking - Multi-Conditional Generation” workflow to automatically generate dual-target Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) inhibitor candidates with the Envelope protein (Epro) and Main protease (Mpro) selected as the two target proteins. MLP stands for Multi-Layer Perceptron. KLD stands for Kullback-Leibler divergence. X, Y, Z stand for atom-wise features, condition vector and element type respectively. • The 3D equivariant conditional generative neural network was used to generate the dual-target SARS-CoV-2 inhibitors. • Our preset affinity conditions have an effective enrichment of molecules with strong target affinity in the generated set. • Our generated dual-target candidate inhibitors exhibit strong affinities for both the Envelope protein and Main protease. • Our generated dual-target candidate inhibitors show good pharmacokinetic properties and structural diversity.