Estimating Protein-Ligand Relative Binding Affinities using the Site-Identification by Ligand Competitive Saturation Approach

Predicting relative protein-ligand binding affinities is a central pillar of lead optimization efforts in structure-based drug design. The Site Identification by Ligand Competitive Saturation (SILCS) methodology based on functional group affinity patterns the form free energy maps that may be used to compute poses and affinities. In present study, we analyze, compare discuss results obtained from SILCS for set eight target proteins as reported originally Wang et al. (J. Am. Chem. Soc. 2015, 137, 2695-2703) using perturbation (FEP) methods conjunction with enhanced sampling cycle closure corrections. These targets have been subsequently studied many other authors efficacy their method while comparing outcomes this work, total 407 ligands include specific analysis subset 199 considered previously. Using can achieve an average accuracy up 77% 74% when considering 421 ligands, respectively, rank-ordering ligand calculated percent correct metrics. This increases 82% 80%, atomic contributions are optimized Bayesian Markov-Chain Monte Carlo approach. Overall, yields similar or better-quality predictions terms different metric variables compared current FEP approaches significant computational savings. further validate accurate, computationally efficient tool support discovery.