Predicting target profiles with confidence as a service using docking scores

Journal of Cheminformatics

Laeeq Ahmed¹  Erwin Laure¹  Ola Spjuth²  Hiba Alogheli²  Arvid Berg²  Staffan Arvidsson McShane²  Jonathan Alvarsson²  Wesley Schaal²  Anders Larsson³ 

[1] Department of Electrical Engineering and Computational Science, Royal Institute of Technology (KTH), Lindstedtsvägen 5, 10044, Stockholm, Sweden;Department of Pharmaceutical Biosciences, Uppsala University, Box 591, 75124, Uppsala, Sweden;National Bioinformatics Infrastructure Sweden (NBIS), Department of Cell and Molecular Biology, Uppsala University, Box 596, 75124, Uppsala, Sweden;

关键词: Predicted target profiles;    Virtual screening;    Drug discovery;    Conformal prediction;    AutoDock Vina;    Apache Spark;   

DOI  :  10.1186/s13321-020-00464-1

来源: Springer

【 摘 要 】

BackgroundIdentifying 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.ContributionsWe 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.ResultsThe 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.

【 授权许可】

CC BY