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.

In Silico Identification of Novel Aromatic Compounds as Potential HIV-1 Entry Inhibitors Mimicking Cellular Receptor CD4.

Despite recent progress in the development of novel potent HIV-1 entry/fusion inhibitors, there are currently no licensed antiviral drugs based on inhibiting the critical interactions of the HIV-1 envelope gp120 protein with cellular receptor CD4. In this connection, studies on the design of new small-molecule compounds able to block the gp120-CD4 binding are still of great value. In this work, in silico design of drug-like compounds containing the moieties that make the ligand active towards gp120 was performed within the concept of click chemistry. Complexes of the designed molecules bound to gp120 were then generated by molecular docking and optimized using semiempirical quantum chemical method PM7. Finally, the binding affinity analysis of these ligand/gp120 complexes was performed by molecular dynamic simulations and binding free energy calculations. As a result, five top-ranking compounds that mimic the key interactions of CD4 with gp120 and show the high binding affinity were identified as the most promising CD4-mimemic candidates. Taken together, the data obtained suggest that these compounds may serve as promising scaffolds for the development of novel, highly potent and broad anti-HIV-1 therapeutics.