ArticleJournal of chemical theory and computation2026
Accelerated Combinatorial Drug Design for Human Immunodeficiency Virus Resistance through Seeded Multisite λ-Dynamics.
Article in Journal of chemical theory and computation, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
The seemingly endless emergence of drug-resistant mutations poses a significant challenge in developing effective therapeutics, particularly for rapidly evolving pathogens like human immunodeficiency virus (HIV). Understanding how small molecular changes can confer resilience against diverse protein mutants is crucial for next-generation drug design. In the work presented here, we apply multisite λ-dynamics (MSλD) to navigate a chemical space of unprecedented size of over 12,000 protein-ligand combinations of HIV-1 Reverse Transcriptase (HIV-RT) and non-nucleoside inhibitors. We simultaneously explore the indole and indolizine inhibitor scaffolds against the wild-type protein, as well as several key resistance-conferring mutations at residues 181 and 188 within the active site. Our simulations show that smaller substituents, such as hydrogen, fluorine, or chlorine atoms, are preferred at all three sites on both drug scaffolds. We find that the binding affinity rankings of the strongest inhibitors remain highly conserved across the entire mutational panel, supporting the identification of truly resilient universal binders. Furthermore, the indolizine scaffold predicts a more favorable binding landscape across the mutants compared to the indole scaffold, suggesting a higher intrinsic resilience. We also map the limits of therapeutic efficacy, which show the Y188I mutation as a resistance hotspot that markedly reduces binding affinity for all tested compounds. To efficiently sample this vast alchemical and mutational space, we introduce a bias seeding method that leverages solvent-phase free energy simulations, calculations already necessary for estimating the relative free energies. This work establishes a powerful and efficient framework for understanding drug resistance landscapes and guiding the design of robust antiviral therapies capable of overcoming mutation-driven escape in systems like HIV-RT.
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