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Automated Framework for Developing Predictive Machine Learning Models for Data-Driven Drug Discovery

submitted on 05.05.2020 and posted on 07.05.2020 by Bruno J. Neves, José T. Moreira-Filho, Arthur C. Silva, Joyce V. V. B. Borba, Melina Mottin, Vinicius Alves, Rodolpho C. Braga, Eugene Muratov, Carolina Andrade
In this manuscript we describe the development of an automated framework for the curation of chemogenomics data and to develop QSAR models for virtual screening using the open-source KNIME software. The workflow includes four modules: (i) dataset preparation and curation; (ii) chemical space analysis and structure-activity relationships (SAR) rules; (iii) modeling; and (iv) virtual screening (VS). As case studies, we applied these workflows to four datasets associated with different endpoints. The implemented protocol can efficiently curate chemical and biological data in public databases and generates robust QSAR models. We provide scientists a simple and guided cheminformatics workbench following the best practices widely accepted by the community, in which scientists can adapt to solve their research problems. The workflows are freely available for download in GitHub.


CNPq, FAPEG, FAPESP, CAPES, and L'Oreal-UNESCO-ABC For Women In Science


Email Address of Submitting Author


University of North Carolina at Chapel Hill


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

The authors declare no conflicts of interest.