Generation of SARS-CoV-2 dual-target candidate inhibitors through 3D equivariant conditional generative neural networks

SARS-CoV-2 mutations are influenced by random and uncontrollable factors, and the risk of the next 3 widespread epidemic remains. Dual-target drugs that synergistically act on two targets exhibit strong 4 therapeutic effects and advantages against mutations. In this study, a novel computational workflow was 5 developed to design dual-target SARS-CoV-2 candidate inhibitors with the Envelope protein and Main 6 protease selected as the two target proteins. The drug-like molecules of our self-constructed 3D scaffold 7 database were used as high-throughput molecular docking probes for feature extraction of two target protein 8 pockets. A multi-layer perceptron (MLP) was employed to embed the binding affinities into a latent space as 9 conditional vectors to control conditional distribution. Utilizing a conditional generative neural network, cG- 10 SchNet, with 3D Euclidean group (E3) symmetries, the conditional probability distributions of molecular 3D 11 structures were acquired and a set of novel SARS-CoV-2 dual-target candidate inhibitors were generated. 12 The 1D probability, 2D joint probability, and 2D cumulative probability distribution results indicate that the 13 generated sets are significantly enhanced compared to the training set in the high binding affinity area. 14 Among the 201 generated molecules, 42 molecules exhibited a sum binding affinity exceeding 17.0 kcal/mol 15 while 9 of them having a sum binding affinity exceeding 19.0 kcal/mol, demonstrating structure diversity 16 along with strong dual-target affinities, good ADMET properties, and ease of synthesis. Dual-target drugs 17 are rare and difficult to find, and our “High-Throughput Docking - Multi-Conditional Generation” workflow 18 offers a wide range of options for designing or optimizing potent dual-target SARS-CoV-2 inhibitors.