Condorcet and borda count fusion method for ligand-based virtual screening

Abstract

BackgroundIt 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.ResultsThe 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.ConclusionsSimulated 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.

Implementing Relevance Feedback for Content-Based Medical Image Retrieval

“…However, using the Kendall W concordance test [33], a more quantitative approach is possible. This test was developed to measure the level of agreement between multiple sets of rankings of the same set of objects; we used this kind of test in our previous works [34], [35] to evaluate the effectiveness of different retrieval methods. The image classes were considered judges in the present context, and the recall rates of the different retrieved methods were considered objects.…”

TEMPy: a Python library for assessment of three-dimensional electron microscopy density fits

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Query Expansion Based on Top-Ranked Images for Content-Based Medical Image Retrieval

“…Similarly Figure 6 shows the recall and precision graph for the PH2 dataset. Kendall W concordance test [38] provides more quantitative testing and it has been used in our previous works [39][40][41][42]. This test was developed to measure the level of agreement between multiple sets of rankings of the same set of objects and it was used to evaluate the effectiveness of different retrieval methods.…”