Deep Neural Network Architecture for Drug-Target Interaction Prediction



The discovery of potential Drug-Target Interactions (DTIs) is a determining step in the drug discovery and repositioning process, as the effectiveness of the currently available antibiotic treatment is declining. Successful approaches have been presented to solve this problem but seldom protein sequences and structured data are used together. We present a deep learning architecture model, which exploits the particular ability of Convolutional Neural Networks (CNNs) to obtain 1D representations from protein amino acid sequences and SMILES (Simplified Molecular Input Line Entry System) strings. The results achieved demonstrate that using CNNs to obtain representations of the data, instead of the traditional descriptors, lead to improved performance.


Deep Learning, Bioinformatics


Computational Drug Discovery

Related Project

D4 - Deep Drug Discovery and Deployment (PI: Bernardete Ribeiro; co-PI: Joel P. Arrais)


Artificial Neural Networks and Machine Learning – ICANN 2019, September 2019


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