Logistic Regression Method for Ligand Discovery

Chian Chen, Hsiuying Wang*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


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.

Original languageEnglish
Pages (from-to)934-940
Number of pages7
JournalJournal of Computational Biology
Issue number6
StatePublished - 1 Jun 2020


  • docking
  • logistic regression
  • protein-specific scoring method


Dive into the research topics of 'Logistic Regression Method for Ligand Discovery'. Together they form a unique fingerprint.

Cite this