Predicting target profiles with confidence as a service using docking scores

• Published in: Journal of cheminformatics Vol. 12; no. 1; pp. 1 - 11
• Main Authors: Ahmed, Laeeq, Alogheli, Hiba, McShane, Staffan Arvidsson, Alvarsson, Jonathan, Berg, Arvid, Larsson, Anders, Schaal, Wesley, Laure, Erwin, Spjuth, Ola
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 15.10.2020; BioMed Central Ltd; BMC
• Subjects: Apache Spark; AutoDock Vina; Chemistry; Chemistry and Materials Science; Computational Biology/Bioinformatics; Computer Applications in Chemistry; Conformal prediction; Digital libraries; Documentation and Information in Chemistry; Drug discovery; Machine learning; Methodology; Predicted target profiles; Theoretical and Computational Chemistry; Typing Ontology (CiTO) Pilot; Virtual screening; Web services; Virtual screening; Drug discovery; Predicted target profiles; AutoDock Vina; Conformal prediction; Apache Spark
• ISSN: 1758-2946; 1758-2946
• DOI: 10.1186/s13321-020-00464-1

Background Identifying and assessing ligand-target binding is a core component in early drug discovery as one or more unwanted interactions may be associated with safety issues. Contributions We present an open-source, extendable web service for predicting target profiles with confidence using machine learning for a panel of 7 targets, where models are trained on molecular docking scores from a large virtual library. The method uses conformal prediction to produce valid measures of prediction efficiency for a particular confidence level. The service also offers the possibility to dock chemical structures to the panel of targets with QuickVina on individual compound basis. Results The docking procedure and resulting models were validated by docking well-known inhibitors for each of the 7 targets using QuickVina. The model predictions showed comparable performance to molecular docking scores against an external validation set. The implementation as publicly available microservices on Kubernetes ensures resilience, scalability, and extensibility.