An evolutionary approach with pharmacophore-based scoring functions for virtual database screening

Jinn-Moon Yang*, Tsai Wei Shen, Yen Fu Chen, Yi Yuan Chiu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

We have developed a new tool for virtual database screening. This tool, referred to as the Generic Evolutionary Method for molecular DOCKing (GEMDOCK), combines an evolutionary approach and a new pharmacophore-based scoring function. The former integrates discrete and continuous global search strategies with local search strategies to speed up convergence. The latter simultaneously serves as the scoring function of both molecular docking and post-docking analysis to improve the number of the true positives. We accessed the accuracy of our approach on HSV-1 thymidine kinase using a ligand database on which competing tools were evaluated. The accuracies of our predictions were 0.54 for the GH score and 1.62% for the false positive rate when the true positive rate was 100%. We found that our pharmacophore-based scoring function indeed is able to reduce the number of the false positives. These results suggest that GEMDOCK is robust and can be a useful tool for virtual database screening.

Keywords

  • Protein Data Bank
  • Molecular Docking
  • Root Mean Square Deviation
  • Thymidine Kinase
  • Virtual Screening

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