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Abstract

PREDICTION OF PLASMA PROTEIN BINDING AFFINITY BY SUPPORT VECTOR MACHINE AND ARTIFICIAL NEURAL NETWORK

Abhishek Singh Chauhan*, Utkarsh Raj, Pritish K.Varadwaj

ABSTRACT

Plasma Protein Binding plays a major role in pharmacokinetics. In this work, we selected drug distribution prediction by support vector statistical learning. Drugs which attach with high attraction to Plasma protein means low drug distribution. If drug binds with low affinity to plasma protein means high drug distribution. Our interest is in high drug distribution. 715 drugs are drug distribution related drug. We have divided 715 drugs in high ppb drug and low ppb drug with help of statistical learning methods. We used two statistical learning methods these are SVM along with ANN. The major idea of machine learning is classifying data into two classes that is high plasma protein binding compound and LPPB compound. In machine learning, feature selection or molecular descriptor is very important footstep. We used TSAR, DRAGON and SCHRODINGER software, which are applicable on molecular file format like SDF to extract 295 descriptors from them. Next we selected 95 descriptors through different feature selection method. Our approach shows that if any one gives new data related to ppb, our predictive model give the class. If anyone gives the test data, our model tells about the high ppb or low ppb class. We got more accuracy in ANN in comparison to SVM.

Keywords: ANN (Artificial Neural Network), SVM (Support Vector Machine), LPPB (Low Plasma Protein Binding), HPPB (High Plasma Protein Binding), FFBP (Feed Forward Back propagation).


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