Increase Docking Score Screening Power by Simple Fusion With CNNscore

Abstract

Scoring functions (SFs) of molecular docking is a vital component of structure-based virtual screening (SBVS). Traditional SFs yield their inherent shortage for idealized approximations and simplifications predicting the binding affinity. Complementarily, SFs based on deep learning (DL) have emerged as powerful tools for capturing intricate features within protein-ligand (PL) interactions. We here present a docking-score fusion strategy that integrates pose scores derived from GNINA's convolutional neural network (CNN) with traditional docking scores. Extensive validation on diverse datasets has shown that by means of multiplying Watvina docking score by CNNscore demonstrates state-of-the-art screening power. Furthermore, in a reverse practice, our docking-score fusion technique was incorporated into the virtual screening (VS) workflow aimed at identifying inhibitors of the challenging target TYK2. Two promising hits with IC₅₀ 9.99 μM and 13.76 μM in vitro were identified from nearly 12 billion molecules.