Generative AI develops potent HIV inhibitors, revealing potential for treating drug-resistant strains.
Guided by the pharmacophore-oriented molecular generation platform PhoreGen, we employed rilpivirine (RPV) as a lead compound to generate 300 structurally diverse analogs that preserve key pharmacophoric features. Subsequent drug-likeness evaluation, molecular docking score, and synthetic feasibility led to the identification of compound No.102 (A19), which displayed potent inhibition activity against WT HIV-1 (EC₅₀ = 3.15 nM) and low cytotoxicity (CC₅₀ > 335 μM). Subsequent structure-activity relationship optimization identified A24, which demonstrated robust activity against clinically relevant drug-resistant HIV-1 mutants (EC₅₀ = 2.07-28.8 nM). Notably, compared to RPV, A24 exhibited significantly reduced cytotoxicity (CC₅₀ = 82.3 μM vs. 3.98 μM) and enhanced potency against the Y188L (EC₅₀ = 28.8 nM vs. 79.4 nM) and F227L + V106A (EC₅₀ = 19.0 nM vs. 81.6 nM) mutants. Pharmacokinetic evaluations revealed that A24 exhibited attenuated CYP enzyme inhibition (IC₅₀ ≥ 1.88 μM), reduced hERG-related toxicity (IC₅₀ = 2.633 μM), and improved metabolic stability (human t1/2 = 39.1 min). Collectively, these favorable properties position A24 as a promising NNRTI for further development.
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Zhang et al. (2026) studied this question.
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