Application of deep learning and molecular modeling to identify small drug-like compounds as potential HIV-1 entry inhibitors.

A generative adversarial autoencoder for the rational design of potential HIV-1 entry inhibitors able to block CD4-binding site of the viral envelope protein gp120 was developed. To do this, the following studies were carried out: (i) an autoencoder architecture was constructed; (ii) a virtual compound library of potential anti-HIV-1 agents for training the neural network was formed by the concept of click chemistry allowing one to generate a large number of drug candidates by their assembly from small modular units; (iii) molecular docking of all compounds from this library with gp120 was made and calculations of the values of binding free energy were performed; (iv) molecular fingerprints of chemical compounds from the training dataset were generated; (v) training of the developed autoencoder was implemented followed by the validation of this neural network using more than 21 million molecules from the ZINC15 database. As a result, three small drug-like compounds that exhibited the high-affinity binding to gp120 were identified. According to the data from molecular docking, machine learning, quantum chemical calculations, and molecular dynamics simulations, these compounds show the low values of binding free energy in the complexes with gp120 similar to those calculated using the same computational protocols for the HIV-1 entry inhibitors NBD-11021 and NBD-14010, highly potent and broad anti-HIV-1 agents presenting a new generation of the viral CD4 antagonists. The identified CD4-mimetic candidates are suggested to present good scaffolds for the design of novel antiviral drugs inhibiting the early stages of HIV-1 infection.