BINDING AFFINITY PREDICTION MODELS FOR SPINOCEREBELLAR ATAXIA USING SUPERVISED LEARNING

Abstract

Spinocerebellar 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.