Abstrait

Molecular diagnosis: Drug target evaluation based on deep neural network prediction techniques

AR Junejo

With the continuous improvement of computer performance and related technology, the combination of Artificial Intelligence (AI) and Bioinformatics can find new drugs more efficiently. However, most of the current researches focus on the prediction of drug target binding affinity and the determination of drug targets. In this work, the quantitative prediction model of drug target is established by combining of Network Bioinformatics and Deep Neural Network (DNN). It includes a regression model for predicting the strength of drug target binding affinity and a classification model for predicting the direction of drug target. After collecting, processing, and matching the data in the major bioinformatics databases, a classification and a regression model are built based on KERAS online library. Moreover, the performance of the model is verified by cross-validation techniques. Finally, the prediction of the strength and direction of binding affinity between drugs and targets is achieved