Computational studies of protein–drug binding affinity changes upon mutations in the drug target

Mutations that lead to drug resistance limit the efficacy of antibiotics, antiviral drugs, targeted cancer therapies, and other treatments. Accurately calculating protein–drug binding affinity changes upon mutations in target is high interest as this can yield a better understanding into how such drive drug-resistance, especially when mutation question does not directly interfere with drug. The main aim article provide an up-to-date reference on computational tools are available for calculation Gibbs energy (free energy) mutation, their strengths, limitations. methods discussed include free calculations perturbation, thermodynamic integration, multistate Bennett acceptance ratio), analysis molecular dynamics simulations (linear interaction energy, mechanics [MM]/Poisson–Boltzmann solvated area, MM/generalized Born area), involve quantum mechanical (including QM/MM). possibility use machine learning also introduced. Given benefit accurately depends comparing calculated values experimental measurements, brief survey observables provided. Examples studies go beyond given. Factors need be addressed by chemist potential pitfalls at length. This categorized under: Structure Mechanism > Computational Biochemistry Biophysics Molecular Statistical Mechanics Free Energy Methods Interactions