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Drug Repositioning

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Drug-Target Interaction Prediction via Deep Multimodal Graph and Structural Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Drug-target interaction (DTI) prediction speeds up drug repurposing, accelerates drug screening, and reduces drug design timeframe. Previous DTI prediction frameworks lack consideration for the multimodal nature of DTI, advanced feature representatio...

GENNDTI: Drug-Target Interaction Prediction Using Graph Neural Network Enhanced by Router Nodes.

IEEE journal of biomedical and health informatics
Identifying drug-target interactions (DTI) is crucial in drug discovery and repurposing, and in silico techniques for DTI predictions are becoming increasingly important for reducing time and cost. Most interaction-based DTI models rely on the guilt-...

SS-DTI: A deep learning method integrating semantic and structural information for drug-target interaction prediction.

Journal of bioinformatics and computational biology
Drug-target interaction (DTI) prediction is pivotal in drug discovery and repurposing, providing a more efficient alternative to traditional wet-lab experiments by saving time and resources and expediting the identification of potential targets. Curr...

Relational similarity-based graph contrastive learning for DTI prediction.

Briefings in bioinformatics
As part of the drug repurposing process, it is imperative to predict the interactions between drugs and target proteins in an accurate and efficient manner. With the introduction of contrastive learning into drug-target prediction, the accuracy of dr...

H2GnnDTI: hierarchical heterogeneous graph neural networks for drug-target interaction prediction.

Bioinformatics (Oxford, England)
MOTIVATION: Identifying drug-target interactions (DTIs) is a crucial step in drug repurposing and drug discovery. The significant increase in demand and the expensive nature for experimentally identifying DTIs necessitate computational tools for auto...

Leveraging machine learning for drug repurposing in rheumatoid arthritis.

Drug discovery today
Rheumatoid arthritis (RA) presents a significant challenge in clinical management because of the dearth of effective drugs despite advances in understanding its mechanisms. Drug repurposing has emerged as a promising strategy to address this gap, off...

Multidependency Graph Convolutional Networks and Contrastive Learning for Drug Repositioning.

Journal of chemical information and modeling
The goal of drug repositioning is to expedite the drug development process by finding novel therapeutic applications for approved drugs. Using multifeature learning, different computational drug repositioning techniques have recently been introduced ...

Applications of Artificial Intelligence in Drug Repurposing.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Drug repurposing identifies new therapeutic uses for the existing drugs originally developed for different indications, aiming at capitalizing on the established safety and efficacy profiles of known drugs. Thus, it is beneficial to bypass of early s...

5-Repurposed Drug Candidates Identified in Motor Neurons and Muscle Tissues with Amyotrophic Lateral Sclerosis by Network Biology and Machine Learning Based on Gene Expression.

Neuromolecular medicine
Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that leads to motor neuron degeneration, muscle weakness, and respiratory failure. Despite ongoing research, effective treatments for ALS are limited. This study aimed to...

A deep learning and molecular modeling approach to repurposing Cangrelor as a potential inhibitor of Nipah virus.

Scientific reports
Deforestation, urbanization, and climate change have significantly increased the risk of zoonotic diseases. Nipah virus (NiV) of Paramyxoviridae family and Henipavirus genus is transmitted by Pteropus bats. Climate-induced changes in bat migration pa...