title: Condorcet and borda count fusion method for ligand-based virtual screening.
abstract: BACKGROUND
  It is known that any individual similarity measure will not always give the best recall of active molecule structure for all types of activity classes. Recently, the effectiveness of ligand-based virtual screening approaches can be enhanced by using data fusion. Data fusion can be implemented using two different approaches: group fusion and similarity fusion. Similarity fusion involves searching using multiple similarity measures. The similarity scores, or ranking, for each similarity measure are combined to obtain the final ranking of the compounds in the database.
  
  
  RESULTS
  The Condorcet fusion method was examined. This approach combines the outputs of similarity searches from eleven association and distance similarity coefficients, and then the winner measure for each class of molecules, based on Condorcet fusion, was chosen to be the best method of searching. The recall of retrieved active molecules at top 5% and significant test are used to evaluate our proposed method. The MDL drug data report (MDDR), maximum unbiased validation (MUV) and Directory of Useful Decoys (DUD) data sets were used for experiments and were represented by 2D fingerprints.
  
  
  CONCLUSIONS
  Simulated virtual screening experiments with the standard two data sets show that the use of Condorcet fusion provides a very simple way of improving the ligand-based virtual screening, especially when the active molecules being sought have a lowest degree of structural heterogeneity. However, the effectiveness of the Condorcet fusion was increased slightly when structural sets of high diversity activities were being sought.
authors.affiliation: Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai 81310, Malaysia ; Faculty of Engineering, Karary University, Khartoum 12304, Sudan.
authors.forename: Ali
authors.initials: A
authors.lastname: Ahmed
authors.affiliation: Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai 81310, Malaysia.
authors.forename: Faisal
authors.initials: F
authors.lastname: Saeed
authors.affiliation: Soft Computing Research Group, Faculty of Computing, Universiti Teknologi Malaysia, Skudai 81310, Malaysia.
authors.forename: Naomie
authors.initials: N
authors.lastname: Salim
authors.affiliation: Computer Science Department, Hodeidah University, Hodeidah, Yemen.
authors.forename: Ammar
authors.initials: A
authors.lastname: Abdo
keywords: Data fusion; Similarity coefficients; Similarity searching; Virtual screening