A Gammerman

Alexander Gammerman

AN Jain

C-C Chang

DC Weis

EY Chang

F Pedregosa

G Shafer

Ilia Nouretdinov

J-L Faulon

K Woodsend

Paolo Toccaceli

V Monev

V Vovk

Y Wang

Y You

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Annals of Mathematics and Artificial Intelligence

Conformal prediction of biological activity of chemical compounds

The paper presents an application of Conformal Predictors to a chemoinformatics problem of predicting the biological activities of chemical compounds. The paper addresses some speciﬁc challenges in this domain: a large number of compounds (training examples), highdimensionality 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 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. This approach allowed us to identify the most likely active compounds for a given biological target and present them in a ranking order

Conformal prediction of biological activity of chemical compounds

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Royal Holloway - Pure