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Fig. 1 | Journal of Cheminformatics

Fig. 1

From: Efficient iterative virtual screening with Apache Spark and conformal prediction

Fig. 1

Workflow of CPVS. Signatures were generated for the whole dataset with two copies named Ds and DsComplete. An initial sample of DsInit number of molecules was randomly taken from Ds and docked against a chosen receptor and scores were calculated. To form a training set, docking scores were converted to class labels {0} and {1} representing ‘low-scoring’ and ‘high-scoring’ ligands, respectively. This was done using a 10-bin histogram of the docking scores where labels were assigned to ligands in different bins. An SVM-based conformal predictor model was trained on the training set and predictions were made on the whole Dataset DsComplete. The molecules were classified as ‘low-scoring’ ligands {0}, ‘high-scoring’ ligands {1} and 'unknown'. The predicted ‘low-scoring’ ligands were removed from Ds in each iteration and were hence never docked. Model efficiency was computed by finding the ratio of single label predictions [30], i.e., {0} and {1} against all predictions. The process was then repeated iteratively with a smaller data sample DsIncr from Ds which was docked and labeled, and the model was re-trained until it reached an acceptable efficiency. Thereafter all remaining ‘high-scoring’ ligands were docked. The scores of all docked molecules were sorted and accuracy for top 30 molecules was computed against the results from an experiment where all molecules were docked [9]

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