# Conformal Predictors for Compound Activity Prediction

arXiv: https://arxiv.org/abs/1603.04506 · HTML: https://mlsanity.com/paper/1603.04506/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Mar 2016.

## Built on by (most cited first, 1 of 1)

- [Uncertainty quantification for improving radiomic-based models in radiation pneumonitis prediction](https://mlsanity.com/paper/2412.19511/) (2024)

## Abstract (arXiv)

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.