AIMC Topic: Drug Repositioning

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In-silico study of approved drugs as potential inhibitors against 3CLpro and other viral proteins of CoVID-19.

PloS one
The global pandemic, due to the emergence of COVID-19, has created a public health crisis. It has a huge morbidity rate that was never comprehended in the recent decades. Despite numerous efforts, potent antiviral drugs are lacking. Repurposing of dr...

GraphCF: Drug-target interaction prediction via multi-feature fusion with contrastive graph neural network.

Artificial intelligence in medicine
Drug-target interaction (DTI) is paramount in drug discovery and repurposing, which involves screening for effective candidate drugs by targeting specific proteins. Existing methods often focus on one or two representations of drugs or targets, and l...

MTGNN: A Drug-Target-Disease Triplet Association Prediction Model Based on Multimodal Heterogeneous Graph Neural Networks and Direction-Aware Metapaths.

Journal of chemical information and modeling
The forecasting of drug-target interactions (DTIs) is a crucial element in the domain of drug repositioning. Current methodologies, primarily based on dual-branch architectures or graph neural networks (GNNs), typically model binary associations─spec...

Prediction of drug-target interactions based on substructure subsequences and cross-public attention mechanism.

PloS one
Drug-target interactions (DTIs) play a critical role in drug discovery and repurposing. Deep learning-based methods for predicting drug-target interactions are more efficient than wet-lab experiments. The extraction of original and substructural feat...

Novel Antimicrobials from Computational Modelling and Drug Repositioning: Potential Strategies to Increase Therapeutic Arsenal Against Antimicrobial Resistance.

Molecules (Basel, Switzerland)
Antimicrobial resistance (AMR) is one of the most significant public health threats today. The need for new antimicrobials against multidrug-resistant infections is growing. The development of computational models capable of predicting new drug-targe...

Cangrelor and AVN-944 as repurposable candidate drugs for hMPV: analysis entailed by AI-driven in silico approach.

Molecular diversity
Human metapneumovirus (hMPV) primarily causes respiratory tract infections in young children and older adults. According to the 2024 Human Pneumonia Etiology Research for Child Health (PERCH) study, hMPV is the second leading common cause of pneumoni...

Integration of machine learning and experimental validation reveals new lipid-lowering drug candidates.

Acta pharmacologica Sinica
Hyperlipidemia, a major risk factor for cardiovascular diseases, is associated with limitations in clinical lipid-lowering medications. Drug repurposing strategies expedite the research process and mitigate development costs, offering an innovative a...

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...

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...

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 ...