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arXiv · 1603.04506

Conformal Predictors for Compound Activity Prediction

Paolo Toccacheli·Ilia Nouretdinov·Alexander Gammerman
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Abstract

The paper presents an application of Conformal Predictors to a chemoinformatics problem of identifying activities of chemical compounds. The paper addresses some specific challenges of this domain: a large number of compounds (training examples), high-dimensionality of feature space, sparseness and a strong class imbalance. A variant of conformal predictors called Inductive Mondrian Conformal Predictor is applied to deal with these challenges. Results are presented for several non-conformity measures (NCM) extracted from underlying algorithms and different kernels. A number of performance measures are used in order to demonstrate the flexibility of Inductive Mondrian Conformal Predictors in dealing with such a complex set of data. Keywords: Conformal Prediction, Confidence Estimation, Chemoinformatics, Non-Conformity Measure.

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Paolo Toccacheli, Ilia Nouretdinov, Alexander Gammerman. 2016-03-14. Conformal Predictors for Compound Activity Prediction. https://arxiv.org/abs/1603.04506

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