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bioRxiv · 10.1101/2023.11.21.568108

CPSign - Conformal Prediction for Cheminformatics Modeling

McShane, S. A.·Norinder, U.·Alvarsson, J.·Ahlberg, E.·Carlsson, L.·Spjuth, O.
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Abstract

Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputs of machine learning models and producing valid prediction intervals. We here present the open source software CPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classification and regression, and probabilistic prediction with the Venn-ABERS methodology. The main chemical representation is signatures but other types of descriptors are also supported. The main modeling methodology is support vector machines (SVMs), but additional modeling methods are supported via an extension mechanism, e.g. DeepLearning4j models. We also describe features for visualizing results from conformal models including calibration and efficiency plots, as well as features to publish predictive models as REST services. We compare CPSign against other common cheminformatics modeling approaches including random forest, and a directed message-passing neural network. The results show that CPSign produces robust predictive performance with comparative predictive efficiency, with superior runtime and lower hardware requirements compared to neural network based models. CPSign has been used in several studies and is in production-use in multiple organizations. The ability to work directly with chemical input files, perform descriptor calculation and modeling with SVM in the conformal prediction framework, with a single software package having a low footprint and fast execution time makes CPSign a convenient and yet flexible package for training, deploying, and predicting on chemical data.

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McShane, S. A., Norinder, U., Alvarsson, J., Ahlberg, E., Carlsson, L., Spjuth, O.. 2023-11-22. CPSign - Conformal Prediction for Cheminformatics Modeling. https://doi.org/10.1101/2023.11.21.568108

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