Similarity-based ligand docking

A common strategy for virtual screening considers a systematic docking of a large library of organic compounds into the target sites in protein receptors. We developed eSimDock, a new approach to ligand docking and binding affinity prediction.  This algorithm employs non-linear machine learning-based scoring functions to improve the accuracy of ligand ranking, and similarity-based binding pose prediction to increase the tolerance to structural imperfections in the target structures. Comprehensive benchmarking calculations demonstrated that the performance of eSimDock is largely unaffected by the deformations of ligand binding regions, thus it represents a practical strategy for across-proteome virtual screening using protein models.