Synergy conformal prediction applied to large-scale bioactivity datasets and in federated learning

• Published in: Journal of cheminformatics 2021-10, Vol.13 (1), p.77-77, Article 77
• Main Authors: Norinder, Ulf, Spjuth, Ola, Svensson, Fredrik
• Format: Article
• Language: English
• Subjects: Biocompatibility; Biological activity; Chemistry; Chemistry and Materials Science; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Confidence; Conformal prediction; Datasets; Decision making; Documentation and Information in Chemistry; Drug discovery; Federated learning; Machine learning; Performance prediction; Predictions; Research Article; Theoretical and Computational Chemistry; Toxicity
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-021-00555-7

Confidence predictors can deliver predictions with the associated confidence required for decision making and can play an important role in drug discovery and toxicity predictions. In this work we investigate a recently introduced version of conformal prediction, synergy conformal prediction, focusing on the predictive performance when applied to bioactivity data. We compare the performance to other variants of conformal predictors for multiple partitioned datasets and demonstrate the utility of synergy conformal predictors for federated learning where data cannot be pooled in one location. Our results show that synergy conformal predictors based on training data randomly sampled with replacement can compete with other conformal setups, while using completely separate training sets often results in worse performance. However, in a federated setup where no method has access to all the data, synergy conformal prediction is shown to give promising results. Based on our study, we conclude that synergy conformal predictors are a valuable addition to the conformal prediction toolbox.