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A probabilistic strategy of data fusion for the classification and virtual screening of anticoccidial drug candidates

Abstract:

In the present work, Dempster-Shafer Theory (DST) was employed for the implementation of a combined strategy for classification and/or virtual screening of potential anticoccidial drug candidates, based on the combination of the information provided by multiple QSAR models which are derived from different molecular structure representations. The application of such a strategy lead to a classification performance superior to the individual use of QSAR models, achieving accuracy/sensibility/specificity values over 94%/86%/96% and 86%/75%/89% on training and pbkp_redicting series, respectively. Parallely, the application of such a strategy lead to values of enrichment metrics significantly superiors to the individual use of QSAR models as virtual screening tools. All these results suggest that the use of DST as the theoretical probabilistic base for the implementation of a combined classification and/or virtual screening strategy can be efficiently employed on the process of discovery and development of novel potential anticoccidial candidates, contributing in this way to overcome the emergence of resistance to current therapies.