| 1. |
- Ahmed, Laeeq, et al.
(författare)
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Predicting target profiles with confidence as a service using docking scores
- 2020
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Ingår i: Journal of Cheminformatics. - : Springer Nature. - 1758-2946. ; 12:1
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Tidskriftsartikel (refereegranskat)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.
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| 2. |
- Laeeq, Ahmed, et al.
(författare)
-
Using Iterative MapReduce for Parallel Virtual Screening
- 2024
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Ingår i: Journal of medical and bioengineering. - : Engineering and Technology Publishing. - 2301-3796.
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Tidskriftsartikel (refereegranskat)abstract
- MapReduce and its different implementations has been successfully used on commodity clusters for analysis of data for problems where the datasets becomes really huge. Virtual Screening is a technique in chemoinformatics used for Drug discovery by searching large libraries of molecule structures, making it a great candidate for MapReduce. However, in this study we used SVM based virtual screening which is resource demanding. Such virtual screening not only have huge datasets, but it is also compute expensive whose complexity can grow at least upto n2. Most SVM based applications use MPI, but MPI has its own limitations such as lack of fault tolerance and low productivity. This study shows that MapReduce can be used effectively for implementing SVM based virtual screening. The results illustrate that MapReduce performs quite well with the increasing nodes on the cluster. For experiments, we have used spark, an iterative MapReduce programming model. We have also provided the flow of program and the results to show the efficiency of iterative MapReduce.
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