Affinity Prediction using Mutated Protein-Ligand Docking with Regression Techniques of SCA
P. R. Asha1, M. S. Vijaya2
1P. R. Asha, Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore, India.
2M. S. Vijaya, Department of Computer Science, PSGR Krishnammal College for Women, Coimbatore, India.
Manuscript received on 03 March 2019 | Revised Manuscript received on 09 March 2019 | Manuscript published on 30 July 2019 | PP: 3642-3648 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1678078219/19©BEIESP | DOI: 10.35940/ijrte.B1678.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Drug 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.
Index Terms: Docking; Kernels; Linear Regression; Ligand; Mutation; Neural Network Regression; Numpy; Polynomial Regression; Regression; Ridge Regression; Scikit learn; Support Vector Regression
Scope of the Article: Regression and Prediction