Virtual screening performance evaluation

Ma, X.H. et al. 2008. Evaluation of virtual screening performance of support vector machines trained by sparsely distributed active compounds. J. Chem. Inf. Model. 48, 1227-1237. 

One of the key tools in evaluating virtual screening performance is some measure of enrichment, whether by ROC curves, enrichment factor and so on. What one wants to know is whether a screening method identifies the actives early in the overall prioritised list of compounds. Truchon and Bayley" have performed a theoretical analysis to show that ROC curves may not be ideal for this purpose, despite their widespread use (and advocacy). A method can get a good ROC score, yet still fail to identify any actives in the top 20%. They propose a new metric based on an enhanced form of the ROC curve called BEDROC, which, via a parameter... 

Geppert H, Vogt M, Bajorath J (2010) Current trends in ligand-based virtual screening molecular representations, data mining methods, new application areas, and performance evaluation. J Chem Inf Model 50(2) 205-216... 

Kirchmair, )., Markt, P., Distinto, S., Wolber, G., and Langer, T. (2008) Evaluation of the performance of 3D virtual screening protocols RMSD comparisons, enrichment assessments, and decoy selection what can we learn from earlier mistakes Journal of Computer-Aided Molecular Design, 22, 213-228. 

Correlation methods for partially ranked data are useful in applications of ligand-based and stracture-based virtual screenings, hi both cases, ranked lists of compounds are produced by any number of methods, which ones are not important. The important point is that lists produced in this manner may not contain the same set of molecules (vide supra). As noted earlier, correlation methods for partially ranked data proved a means of evaluating how the two types of methods performed. This approach can also be applied to pairwise comparisons of compound lists generated by ligand- and... 

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