Enhancing the Reliability of Integrated Consensus Strategies to Boost Docking‐Based Screening Campaigns Using Publicly Available Docking Programs

Abstract

The use of docking-based virtual screening is today an established critical component within the drug discovery pipeline. In the context where the performance of molecular docking has been found to depend on the protein target and the program, consensus docking has been found to be a valuable approach to enhance the performance of high-throughput docking (HTD). We present and evaluate an integrated pose and ranking consensus approach that combines the advantages of pose consensus and the exponential consensus ranking (ECR) approach, using only publicly available docking programs (rDock, DOCK 6, Auto Dock 4, PLANTS, and Vina). Based on a thorough analysis performed to assess the optimal combination of matching poses and ECR thresholds, using a benchmarking set of 50 protein targets of diverse families and different property-matched ligand/decoy libraries, this enhanced pose/ranking consensus approach displayed a notably superior performance than the individual docking programs, and the ECR. This approach was also evaluated in HTD campaigns using larger libraries (∼1.1 million molecules) on six targets, thus obtaining an average improvement of the ECR of about 40%. We thus may say that this pose/ranking consensus methodology can be confidently used in prospective HTD campaigns using free-available docking programs.