DeepCNN-DTI: A Deep Learning Model for Detecting Drug-Target Interactions

Drug target interaction is an important area of drug discovery, development, and repositioning. Knowing that in vitro experiments are time-consuming and computationally expensive, the development of an efficient predictive model is a promising challenge for Drug-Target Interactions (DTIs) prediction...

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Bibliographic Details
Published in2023 9th International Conference on Control, Decision and Information Technologies (CoDIT) pp. 1677 - 1682
Main Authors Ghozzi, Wiem Ben, Chaabani, Abir, Kodia, Zahra, Said, Lamjed Ben
Format Conference Proceeding
LanguageEnglish
Published IEEE 03.07.2023
Subjects
Biological computational model
Biological system modeling
CNN
Computational modeling
Deep learning
Drug-Target Interaction detection
Drugs
Predictive models
Proteins
Sensitivity
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Summary:Drug target interaction is an important area of drug discovery, development, and repositioning. Knowing that in vitro experiments are time-consuming and computationally expensive, the development of an efficient predictive model is a promising challenge for Drug-Target Interactions (DTIs) prediction. Motivated by this problem, we propose in this paper a new prediction model called DeepCNN-DTI to efficiently solve such complex real-world activities. The main motivation behind this work is to explore the advantages of a deep learning strategy with feature extraction techniques, resulting in an advanced model that effectively captures the complex relationships between drug molecules and target proteins for accurate DTIs prediction. Experimental results generated based on a set of data in terms of accuracy, precision, sensitivity, specificity, and F1-score demonstrate the superiority of the model compared to other competing learning strategies.
ISSN:2576-3555
DOI:10.1109/CoDIT58514.2023.10284177