Conformal prediction has seen many applications in pharmaceutical science, being able to calibrate outputsof machine learning models and producing valid prediction intervals. We here present the open source softwareCPSign that is a complete implementation of conformal prediction for cheminformatics modeling. CPSign implements inductive and transductive conformal prediction for classifcation and regression, and probabilistic predictionwith the Venn-ABERS methodology. The main chemical representation is signatures but other types of descriptorsare also supported. The main modeling methodology is support vector machines (SVMs), but additional modelingmethods are supported via an extension mechanism, e.g. DeepLearning4J models. We also describe features for visualizing results from conformal models including calibration and efciency plots, as well as features to publish predictive models as REST services. We compare CPSign against other common cheminformatics modeling approachesincluding random forest, and a directed message-passing neural network. The results show that CPSign producesrobust predictive performance with comparative predictive efciency, with superior runtime and lower hardwarerequirements 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 fles, perform descriptor calculationand 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 fexible package for training, deploying, and predicting on chemical data. CPSign can be downloaded from GitHub at https://github.com/arosbio/cpsign.

Scientifc contribution 

CPSign provides a single software that allows users to perform data preprocessing, modeling and make predictionsdirectly on chemical structures, using conformal and probabilistic prediction. Building and evaluating new modelscan be achieved at a high abstraction level, without sacrifcing fexibility and predictive performance—showcasedwith a method evaluation against contemporary modeling approaches, where CPSign performs on par with a stateof-the-art deep learning based model.