Journal
MOLECULES
Volume 20, Issue 6, Pages 10947-10962Publisher
MDPI
DOI: 10.3390/molecules200610947
Keywords
docking; binding affinity prediction; machine-learning scoring functions
Funding
- A*MIDEX grant - French Government Investissements d'Avenir program [ANR-11-IDEX-0001-02]
- Chinese University of Hong Kong
- Research Grants Council of Hong Kong SAR, China [414413]
Ask authors/readers for more resources
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
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available