Please use this identifier to cite or link to this item: http://localhost:8080/xmlui/handle/123456789/4375
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 SizeFormat 
SUPPORT VECTOR REGRESSION FOR PREDICTING BINDING AFFINITY IN SPINOCEREBELLAR ATAXIA.docx159.93 kBMicrosoft Word XMLView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.