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The discovery of novel drug target (DT) interactions is an important step inthe drug development process. The majority of computer techniques forpredicting DT interactions have focused on binary classification, with the goalof determining whether or not a DT pair interacts. Protein ligand interactions,on the other hand, assume a continuous range of binding strength values, alsoknown as binding affinity, and forecasting this value remains a difficulty. Asthe amount of affinity data in DT knowledge-bases grows, advanced learningtechniques such as deep learning architectures can be used to predict bindingaffinities. In this paper, we present a deep-learning-based methodology forpredicting DT binding affinities using just sequencing information from bothtargets and drugs. The results show that the proposed deep learning-based modelthat uses the 1D representations of targets and drugs is an effective approachfor drug target binding affinity prediction and it does not require additionalchemical domain knowledge to work with. The model in which high-levelrepresentations of a drug and a target are constructed via CNNs that usesresidual skip connections and also with an additional stream to create ahigh-level combined representation of the drug-target pair achieved the bestConcordance Index (CI) performance in one of the largest benchmark datasets,outperforming the recent state-of-the-art method AttentionDTA and many othermachine-learning and deep-learning based baseline methods for DT bindingaffinity prediction that uses the 1D representations of targets and drugs.

ResDTA: Predicting Drug-Target Binding Affinity Using Residual Skip Connections