Prediction and molecular field view of drug resistance in HIV-1 protease mutants

Machine learning-guided design of potent darunavir analogs targeting HIV-1 proteases: A computational approach for antiretroviral drug discovery

In the pursuit of novel antiretroviral therapies for human immunodeficiency virus type-1 (HIV-1) proteases (PRs), recent improvements in drug discovery have embraced machine learning (ML) techniques to guide the design process. This study employs ensemble learning models to identify crucial substructures as significant features for drug development. Using molecular docking techniques, a collection of 160 darunavir (DRV) analogs was designed based on these key substructures and subsequently screened using molecular docking techniques. Chemical structures with high fitness scores were selected, combined, and one-dimensional (1D) screening based on beyond Lipinski's rule of five (bRo5) and ADME (absorption, distribution, metabolism, and excretion) prediction implemented in the Combined Analog generator Tool (CAT) program. A total of 473 screened analogs were subjected to docking analysis through convolutional neural networks scoring function against both the wild-type (WT) and 12 major mutated PRs. DRV analogs with negative changes in binding free energy (ΔΔ G bind ) compared to DRV could be categorized into four attractive groups based on their interactions with the majority of vital PRs. The analysis of interaction profiles revealed that potent designed analogs, targeting both WT and mutant PRs, exhibited interactions with common key amino acid residues. This observation further confirms that the ML model-guided approach effectively identified the substructures that play a crucial role in potent analogs. It is expected to function as a powerful computational tool, offering valuable guidance in the identification of chemical substructures for synthesis and subsequent experimental testing.

Rational design, synthesis and biological evaluation of novel HIV-1 protease inhibitors containing 2-phenylacetamide derivatives as P2 ligands with potent activity against DRV-Resistant HIV-1 variants

A novel kind of potent HIV-1 protease inhibitors, containing diverse hydroxyphenylacetic acids as the P2-ligands and 4-substituted phenyl sulfonamides as the P2' ligands, were designed, synthesized and evaluated in this work. Majority of the target compounds exhibited good to excellent activity against HIV-1 protease with IC50 values below 200 nM. In particular, compound 18d with a 2-(3,4-dihydroxyphenyl) acetamide as the P2 ligand and a 4- methoxybenzene sulfonamide P2' ligand exhibited inhibitory activity IC50 value of 0.54 nM, which was better than that of the positive control darunavir (DRV). More importantly, no significant decline of the potency against HIV-1DRVRS (DRV-resistant mutation) and HIV-1NL4_3 variant (wild type) for 18d was detected. The molecular docking study of 18d with HIV-1 protease (PDB-ID: 1T3R, www.rcsb.org) revealed possible binding mode with the HIV-1 protease. These results suggested the validity of introducing phenol-derived moieties into the P2 ligand and deserve further optimization which was of great value for future discovery of novel HIV-1 protease.