Conformal Predictors for Compound Activity Prediction
This work addresses activity prediction for chemical compounds, which is an incremental application of existing conformal prediction methods to a specific domain.
The paper tackled the problem of predicting chemical compound activities in chemoinformatics by applying Inductive Mondrian Conformal Predictors to handle challenges like large datasets, high-dimensional features, sparsity, and class imbalance, demonstrating flexibility through various performance measures.
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.