title: Low-Quality Structural and Interaction Data Improves Binding Affinity Prediction via Random Forest.
abstract: 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.
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Sha Tin, New Territories 999077, Hong Kong. jackyleehongjian@gmail.com.
authors.forename: Hongjian
authors.initials: H
authors.lastname: Li
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Sha Tin, New Territories 999077, Hong Kong. ksleung@cse.cuhk.edu.hk.
authors.forename: Kwong-Sak
authors.initials: KS
authors.lastname: Leung
authors.affiliation: Department of Computer Science and Engineering, Chinese University of Hong Kong, Sha Tin, New Territories 999077, Hong Kong. mhwong@cse.cuhk.edu.hk.
authors.forename: Man-Hon
authors.initials: MH
authors.lastname: Wong
authors.affiliation: Cancer Research Center of Marseille, INSERM U1068, F-13009 Marseille, France. pedro.ballester@inserm.fr.
authors.forename: Pedro J
authors.initials: PJ
authors.lastname: Ballester
mesh_terms: D008962:Models, Theoretical; D013329:Structure-Activity Relationship
keywords: binding affinity prediction; docking; machine-learning scoring functions