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Item AFFINITY PREDICTION USING MUTATED PROTEIN-LIGAND DOCKING WITH REGRESSION TECHNIQUES OF SCA(Blue Eyes Intelligence Engineering & Sciences Publication, 2019-07) Asha, P R; Vijaya, M SDrug discovery for rare genetic disorder like spinocerebellar ataxia is very complicated in biomedical research. Numerous approaches are available for drug design in clinical labs, but it is time consuming. There is a need for affinity prediction of spinocerebellar ataxia, which will help in facilitating the drug design. In this work, the proteins are mutated with the information available from HGMD database. The repeat mutations are induced manually, and that mutated proteins are docked with ligand. The model is trained with extricated features such as energy profiles, rf-score, autodock vina scores, cyscore and sequence descriptors. Regression techniques like linear, polynomial, ridge, SVM and neural network regression are implemented. The predictive models are built with various regression techniques and the predictive model implemented with support vector regression is compared with support vector regression kernel. Among all regression techniques, SVR performs well than the other regression models.Item SUPPORT VECTOR REGRESSION FOR PREDICTING BINDING AFFINITY IN SPINOCEREBELLAR ATAXIA(Springer Link, 2019) Asha, P R; Vijaya, M SSpinocerebellar 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.Item BINDING AFFINITY PREDICTION MODELS FOR SPINOCEREBELLAR ATAXIA USING SUPERVISED LEARNING(Springer Link, 2018-08-21) Asha, P R; Vijaya, M SSpinocerebellar Ataxia (SCA) is an inherited disorder flow in the family, even when one parent is affected. Disorder arises mainly due to mutations in the gene, which affects the gray matter in the brain and causes neuron degeneration. 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 essential to know how tightly the ligand binds to the protein. In this work, the binding affinity prediction model is built using machine learning. To build the model, features like Binding energy, IC50, Torsional energy and surface area for both ligand and protein are extracted from Auto dock, auto dock vina and PYmol from the complex. A total of 17 structures and 18 drugs were used for building the model. This paper proposes a predictive model using applied mathematics, machine learning regression techniques like rectilinear regression, Artificial neural network (ANN) and Random Forest (RF). Experimental results show that the model built using Random Forest outperforms in predicting the binding affinity.Item AFFINITY PREDICTION USING MUTATED PROTEIN-LIGAND DOCKING WITH REGRESSION TECHNIQUES OF SCA(Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP), 2019-07) Asha, P R; Vijaya, M SDrug discovery for rare genetic disorder like spinocerebellar ataxia is very complicated in biomedical research. Numerous approaches are available for drug design in clinical labs, but it is time consuming. There is a need for affinity prediction of spinocerebellar ataxia, which will help in facilitating the drug design. In this work, the proteins are mutated with the information available from HGMD database. The repeat mutations are induced manually, and that mutated proteins are docked with ligand. The model is trained with extricated features such as energy profiles, rf-score, autodock vina scores, cyscore and sequence descriptors. Regression techniques like linear, polynomial, ridge, SVM and neural network regression are implemented. The predictive models are built with various regression techniques and the predictive model implemented with support vector regression is compared with support vector regression kernel. Among all regression techniques, SVR performs well than the other regression models.