ArticleJournal of chemical information and modeling2025
A Simple Framework for Collaborative Development of Predictive Models Trained on Proprietary Data.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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9 authors.
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Abstract
We present a simple methodology that allows the building and sharing of predictive models without compromising the confidentiality of the structures of the training series. Multiple shared models can be used to obtain ensemble models, providing better coverage of the chemical space and better predictions than the original one. This approach is demonstrated in a collaborative exercise where four pharmaceutical and chemical companies developed predictive models for the AMES mutagenicity end point and shared them to build ensemble models using logical and machine learning algorithms. The results were systematically analyzed and compared, obtaining clear benefits in predictive quality. The method has the advantage of being very simple to execute, using only open-source software, and the possibility to audit the whole process and interchange files to guarantee that no confidential information is exported from the company facilities.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.