Predicting target profiles with confidence as a service using docking scores
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 us...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-10-01
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| Series: | Journal of Cheminformatics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13321-020-00464-1 |
| _version_ | 1819229179364769792 |
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| author | Laeeq Ahmed Hiba Alogheli Staffan Arvidsson McShane Jonathan Alvarsson Arvid Berg Anders Larsson Wesley Schaal Erwin Laure Ola Spjuth |
| author_facet | Laeeq Ahmed Hiba Alogheli Staffan Arvidsson McShane Jonathan Alvarsson Arvid Berg Anders Larsson Wesley Schaal Erwin Laure Ola Spjuth |
| author_sort | Laeeq Ahmed |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-23T11:09:03Z |
| format | Article |
| id | doaj.art-3cc3e6c22dfd49d4824bba59b083b156 |
| institution | Directory Open Access Journal |
| issn | 1758-2946 |
| language | English |
| last_indexed | 2024-12-23T11:09:03Z |
| publishDate | 2020-10-01 |
| publisher | BMC |
| record_format | Article |
| series | Journal of Cheminformatics |
| spelling | doaj.art-3cc3e6c22dfd49d4824bba59b083b1562022-12-21T17:49:24ZengBMCJournal of Cheminformatics1758-29462020-10-0112111110.1186/s13321-020-00464-1Predicting target profiles with confidence as a service using docking scoresLaeeq Ahmed0Hiba Alogheli1Staffan Arvidsson McShane2Jonathan Alvarsson3Arvid Berg4Anders Larsson5Wesley Schaal6Erwin Laure7Ola Spjuth8Department of Electrical Engineering and Computational Science, Royal Institute of Technology (KTH)Department of Pharmaceutical Biosciences, Uppsala UniversityDepartment of Pharmaceutical Biosciences, Uppsala UniversityDepartment of Pharmaceutical Biosciences, Uppsala UniversityDepartment of Pharmaceutical Biosciences, Uppsala UniversityNational Bioinformatics Infrastructure Sweden (NBIS), Department of Cell and Molecular Biology, Uppsala UniversityDepartment of Pharmaceutical Biosciences, Uppsala UniversityDepartment of Electrical Engineering and Computational Science, Royal Institute of Technology (KTH)Department of Pharmaceutical Biosciences, Uppsala UniversityAbstract 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.http://link.springer.com/article/10.1186/s13321-020-00464-1Predicted target profilesVirtual screeningDrug discoveryConformal predictionAutoDock VinaApache Spark |
| spellingShingle | Laeeq Ahmed Hiba Alogheli Staffan Arvidsson McShane Jonathan Alvarsson Arvid Berg Anders Larsson Wesley Schaal Erwin Laure Ola Spjuth Predicting target profiles with confidence as a service using docking scores Journal of Cheminformatics Predicted target profiles Virtual screening Drug discovery Conformal prediction AutoDock Vina Apache Spark |
| title | Predicting target profiles with confidence as a service using docking scores |
| title_full | Predicting target profiles with confidence as a service using docking scores |
| title_fullStr | Predicting target profiles with confidence as a service using docking scores |
| title_full_unstemmed | Predicting target profiles with confidence as a service using docking scores |
| title_short | Predicting target profiles with confidence as a service using docking scores |
| title_sort | predicting target profiles with confidence as a service using docking scores |
| topic | Predicted target profiles Virtual screening Drug discovery Conformal prediction AutoDock Vina Apache Spark |
| url | http://link.springer.com/article/10.1186/s13321-020-00464-1 |
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