Logistic Regression Method for Ligand Discovery

Chian Chen, Hsiuying Wang*

*Corresponding author for this work

研究成果: Article同行評審

摘要

Protein-based virtual screening is integral to the modern drug discovery process. Most protein-based virtual screening experiments are performed using docking programs. The accuracy of a docking program strongly relies on the incorporated scoring function used, which is based on various energy terms. The existing scoring functions deal with the energy terms that use the equal weight function or other weight functions, which do not depend on characteristics of the protein. To improve the existing methods, Lu and Wang proposed a protein-specific scoring function based on a regression analysis that was shown to have higher performance than the existing methods. In this study, we propose a protein-specific scoring approach to select potential ligands based on logistic regression analysis. The performance of our method was evaluated using the Directory of Useful Decoys docked data set, which contains 40 protein targets. The results showed that the proposed method can increase the enrichment factors for most of the 40 protein targets.

原文English
頁(從 - 到)934-940
頁數7
期刊Journal of Computational Biology
27
發行號6
DOIs
出版狀態Published - 1 六月 2020

指紋 深入研究「Logistic Regression Method for Ligand Discovery」主題。共同形成了獨特的指紋。

引用此