Abstract

AbstractThe 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.

Highlights

  • Computational methods for Drug-Target Interactions (DTIs) prediction are divided into 3 main approaches [4], namely ligand based, docking simulation and chemogenomic

  • We propose a deep learning approach to predict DTIs using 1D raw data, amino acids sequences and SMILES

  • We compared our model with different approaches, random forest (RF), a Fully Connected Neural Network (FCNN) architecture and support vector machine (SVM)

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Summary

Introduction

The discovery of new and potential drugs is declining, as there is an increase of the misuse of the available medicine, causing a resistance effect to these kinds of agents [1]. Establishing effective computational methods is decisive to find new leads. Computational methods for DTI prediction are divided into 3 main approaches [4], namely ligand based, docking simulation and chemogenomic. Ligand based approaches are built upon the concept that similar molecules have similar properties and should bind to the same group of proteins [6]. Chemogenomic approaches are based on the chemical, genomic and/or the pharmacological space [8]. Due to the amount of available data and computational power, machine learning [3] and deep learning [9] are pursued over the traditional methods. We propose a deep learning approach to predict DTIs using 1D raw data, amino acids sequences and SMILES. We compared our model with different approaches, random forest (RF), a FCNN architecture and support vector machine (SVM)

Methods
Data Representation
Hyperparameter Optimization Approach
Results

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