DFDTA-MultiAtt: Multi-attention based deep learning ensemble fusion network for drug target affinity prediction

Abstract

An essential step in the drug development process is the accurate detection of drug-target interactions (DTI). The importance of binding affinity values in understanding protein-ligand interactions was previously disregarded, and DTI prediction was only seen as a binary classification problem. In this regard, we introduced the DFDTA-MultiAtt model for predicting the drug target binding affinity in two stages using the structural and sequential information. The first step of the first stage involves retrieving features from sequence data using a bi-directional long short term memory (Bi-LSTM) architecture together with a multi-attention module and dilated convolutional neural network (dilated-CNN) architecture, and the second step features are learnt from structure representation once again using a dilated-CNN. To predict the binding affinity, the second stage uses an ensemble learning model. The proposed model also produces findings with a greater overall accuracy when compared to contemporary state-of-the-art methods. The model generates an enormous +0.006 concordance index (CI) score on the Davis dataset and reduces the mean square error (MSE) by 0.174 on the KIBA dataset.