Li, HongjianHongjianLiProf. LEUNG Kwong SakWong, Man-HonMan-HonWongBallester, Pedro J.Pedro J.Ballester2023-03-152023-03-152015Molecules 2015, 20(6), 10947-109621420-3049http://hdl.handle.net/20.500.11861/7483Open accessAbstract Docking scoring functions can be used to predict the strength of protein-ligand binding. It is widely believed that training a scoring function with low-quality data is detrimental for its predictive performance. Nevertheless, there is a surprising lack of systematic validation experiments in support of this hypothesis. In this study, we investigated to which extent training a scoring function with data containing low-quality structural and binding data is detrimental for predictive performance. We actually found that low-quality data is not only non-detrimental, but beneficial for the predictive performance of machine-learning scoring functions, though the improvement is less important than that coming from high-quality data. Furthermore, we observed that classical scoring functions are not able to effectively exploit data beyond an early threshold, regardless of its quality. This demonstrates that exploiting a larger data volume is more important for the performance of machine-learning scoring functions than restricting to a smaller set of higher data quality.enDockingBinding Affinity PredictionMachine-Learning Scoring FunctionsPeer Reviewed Journal Article10.3390/molecules200610947