DeepDTA: deep drug-target binding affinity interaction prediction

The identification of novel drug-target (DT) interactions is a substantial part of the drug discovery process. Most of the computational methods that have been proposed to predict DT interactions have focused on binary classification, where the goal is to determine whether a DT pair interacts or not. However, protein-ligand interactions assume a continuum of binding strength values, also called binding affinity and predicting this value still remains a challenge.

In this study, we propose a deep-learning based model that uses only sequence information of both targets and drugs to predict DT interaction binding affinities. The few studies that focus on DT binding affinity prediction use either 3D structures of protein-ligand complexes or 2D features of compounds.

One novel approach used in this work is the modeling of protein sequences and compound 1D representations with convolutional neural networks (CNNs). The results show that the proposed deep learning based model that uses the 1D representations of targets and drugs is an effective approach for drug-target binding affinity prediction.

Convolutional Neural Networks (CNNs)



Öztürk, Hakime, Elif Ozkirimli, and Arzucan Özgür. "DeepDTA: deep drug-target interaction binding affinity prediction."
Bioinformatics, accepted for publication (2018).
ECCB2018 Proceedings | arXiv

Source Code
Implementation of the method and datasets that we used for evaluation are available on Github.