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Title: | SUPPORT VECTOR REGRESSION FOR PREDICTING BINDING AFFINITY IN SPINOCEREBELLAR ATAXIA |
Authors: | Asha, P R Vijaya, M S |
Keywords: | Binding affinity Docking Ligand Machine learning Prediction Protein Protein structure |
Issue Date: | 2019 |
Publisher: | Springer Link |
Abstract: | Spinocerebellar ataxia (SCA) is an inherited disorder. It arises mainly due to gene mutations, which affect gray matter in the brain causing neurodegeneration. There are certain types of SCA that are caused by repeat mutation in the gene, which produces differences in the formation of protein sequence and structures. Binding affinity is very essential to know how tightly the ligand binds with the protein. In this work, a binding affinity prediction model is built using machine learning. To build the model, predictor variables and their values such as binding energy, IC50, torsional energy and surface area for both ligand and protein are extracted from the complex using AutoDock, AutoDock Vina and PyMOL. A total of 17 structures and 18 drugs were used for learning the support vector regression (SVR) model. Experimental results proved that the SVR-based affinity prediction model performs better than other regression models. |
URI: | https://link.springer.com/chapter/10.1007/978-981-10-8797-4_19 |
Appears in Collections: | 3.Book Chapter (2) |
Files in This Item:
File | Description | Size | Format | |
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SUPPORT VECTOR REGRESSION FOR PREDICTING BINDING AFFINITY IN SPINOCEREBELLAR ATAXIA.docx | 159.93 kB | Microsoft Word XML | View/Open |
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