h) 2018 - 49 Documents

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    PREDICTING BINDING AFFINITY BASED ON DOCKING MEASURES FOR SPINOCEREBELLAR ATAXIA: A STUDY
    (Springer Link, 2018) Asha, P R; Vijaya, M S
    An obsessive stipulation impairs the regular function or structure of an organ in humans. Spinocerebellar ataxia disorder is a hereditary genetic disorder which is originated by the massive number of sequence variants found in large sets of genes. The mutation in the genes causes many of these disorders. There are certainly no effective drugs to treat those disorders. There are many types of spinocerebellar ataxia, and a better knowledge is required to forecast binding affinity. Binding affinity is crucial to screen the drugs for spinocerebellar ataxia disorder. Accurate identification of binding affinities is a profoundly demanding task. To overcome this issue, a new approach is to be designed in identifying the binding affinity effectively. Due to rapid growth of biological data, there is an increase in the processing time and cost efficiency. This paves the way for challenges in computing. The purpose of machine learning is to excavate beneficial knowledge in distinct to corpus of information and data by constructing effective feasible designs. In this paper, a preface to spinocerebellar ataxia, conventional and innovative strategies involved in predicting binding affinity are discussed.
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    BINDING AFFINITY PREDICTION MODELS FOR SPINOCEREBELLAR ATAXIA USING SUPERVISED LEARNING
    (Springer Link, 2018-08-21) Asha, P R; Vijaya, M S
    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.