Paper Title
Probabilistic Model of Protein-Ligand Interaction

Analysis of protein - small molecule interactions is crucial in the discovery of new drug candidates and lead structure optimization. Small biomolecules (ligands) are highly flexible and may adopt numerous conformations upon binding to the protein. Scoring functions are traditionally used in many docking protocols and have key impact on a quality of structure-based virtual screening. A correct scoring function should be able to guide search algorithm to find and recognize native-like docking poses. In ideal case scoring function should be able to predict binding affinity. Despite extensive research, scoring remains a major challenge in structure-based virtual screening. We apply Stochastic Roadmap Simulation (SRS) and finite absorbing Markov chain theory to build a model of protein-ligand binding process. We propose a computational quantity – time to escape (TTE) from a funnel of attraction around binding site as a measure of binding affinity. The results based on PDBBind Core Set show statistically significant correlation between experimental binding affinity and calculated TTE. Index Terms- protein-ligand interaction, Stochastic Roadmap Simulation, Poisson-Boltzmann equation.