Model docking, which refers to ligand docking into the protein model structures, is becoming a promising avenue in drug discovery with advances in artificial intelligence (AI)-based protein structure prediction. However, a significant challenge remains
even when sampling was successful in model docking, typical docking score functions failed to identify correct solutions for two-thirds of them. This discrepancy between scoring and sampling majorly arises because these scoring functions poorly tolerate minor structural inaccuracies. In this work, we propose a deep neural network named DENOISer to address the scoring challenge in model-docking scenarios. In the network, ligand poses are ranked by the consensus score of two independent subnetworks: the