دورية أكاديمية
Predicting target profiles with confidence as a service using docking scores
| العنوان: | Predicting target profiles with confidence as a service using docking scores |
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| المؤلفون: | Laeeq Ahmed, Hiba Alogheli, Staffan Arvidsson McShane, Jonathan Alvarsson, Arvid Berg, Anders Larsson, Wesley Schaal, Erwin Laure, Ola Spjuth |
| المصدر: | Journal of Cheminformatics, Vol 12, Iss 1, Pp 1-11 (2020) |
| بيانات النشر: | BMC, 2020. |
| سنة النشر: | 2020 |
| المجموعة: | LCC:Information technology LCC:Chemistry |
| مصطلحات موضوعية: | Predicted target profiles, Virtual screening, Drug discovery, Conformal prediction, AutoDock Vina, Apache Spark, Information technology, T58.5-58.64, Chemistry, QD1-999 |
| الوصف: | Abstract 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. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1758-2946 |
| Relation: | http://link.springer.com/article/10.1186/s13321-020-00464-1; https://doaj.org/toc/1758-2946 |
| DOI: | 10.1186/s13321-020-00464-1 |
| URL الوصول: | https://doaj.org/article/3cc3e6c22dfd49d4824bba59b083b156 |
| رقم الأكسشن: | edsdoj.3cc3e6c22dfd49d4824bba59b083b156 |
| قاعدة البيانات: | Directory of Open Access Journals |
Find this article in full text from Springer Verlag. 
Full Text Finder | تدمد: | 17582946 |
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| DOI: | 10.1186/s13321-020-00464-1 |