Prediction of Active Drug Molecule using Back-Propagation Neural Network

Traditional drug designing and discovery processes are a very time consuming, expensive and challenging tasks to produce a new drug in the market. Computer Aided Drug Design (CADD) is a promising approach that is cost effective as well as speeds up the drug designing process. CADD is a computational methods which provides resources for simplifying the design and discovery of a new drug. At molecular level, a drug binds to the target protein and neutralizes the disease. Therefore, identification of active molecules which can bind to the target protein is an essential part of CADD. In this paper, the back propagation neural network model is employed for predicting inactive or active molecules, which provides chemical compounds with desirable properties for drug design. The proposed approach demonstrates 99% prediction accuracy on the dataset which consists of active and inactive molecules taken from PubChem data repository.