AIMC Topic: Drug Repositioning

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VCTatDot and VCTatMLP: novel deep learning models with triadic attention embeddings for synergistic drug combination prediction.

Scientific reports
Computational drug repurposing is vital in drug discovery research because it significantly reduces both the cost and time involved in the drug development process. Additionally, combination therapy-using more than one drug for treatment-can enhance ...

Large Language Model-Enhanced Drug Repositioning Knowledge Extraction via Long Chain-of-Thought: Development and Evaluation Study.

JMIR medical informatics
BACKGROUND: Drug repositioning is a pivotal strategy in pharmaceutical research, offering accelerated and cost-effective therapeutic discovery. However, biomedical information relevant to drug repositioning is often complex, dispersed, and underutili...

The Road to Precision Nanomedicine: An Insight on Drug Repurposing and Advances in Nanoformulations for Treatment of Cancer.

AAPS PharmSciTech
Cancer remains one of the most significant global health challenges, with its burden continuing to rise. The limitations of conventional anticancer therapies caused by the lack of tissue selectivity, demands urgent development of safer and more selec...

A graph neural network-based approach for predicting SARS-CoV-2-human protein interactions from multiview data.

PloS one
The COVID-19 pandemic has demanded urgent and accelerated action toward developing effective therapeutic strategies. Drug repurposing models (in silico) are in high demand and require accurate and reliable molecular interaction data. While experiment...

scKAN: interpretable single-cell analysis for cell-type-specific gene discovery and drug repurposing via Kolmogorov-Arnold networks.

Genome biology
BACKGROUND: Analysis of single-cell RNA sequencing (scRNA-seq) data has revolutionized our understanding of cellular heterogeneity, yet current approaches face challenges in efficiency, interpretability, and connecting molecular insights to therapeut...

Integrative Computational Approaches for TRPV1 Ion Channel Inhibitor Discovery: An Integrated Machine Learning, Drug Repurposing and Molecular Simulation Approach.

Journal of chemical information and modeling
The transient receptor potential vanilloid 1 (TRPV1) ion channel is a key mediator of pain and inflammation, making it a crucial target for developing new analgesics. Despite progress in understanding TRPV1's role, novel modulators that effectively i...

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN.

BMC bioinformatics
BACKGROUND: The exploration of drug-target interactions (DTIs) is a critical step in drug discovery and drug repurposing. Recently, network-based methods have emerged as a prominent research area for predicting DTIs. These methods excel by extracting...

High-throughput behavioral screening in Caenorhabditis elegans using machine learning for drug repurposing.

Scientific reports
Caenorhabditis elegans is a widely used animal model for researching new disease treatments. In recent years, automated methods have been developed to extract mobility phenotypes and analyse, using statistical methods, whether there are differences b...

Deep homo-heterogeneous association mining with hybrid scholars and multidimensional mixed moment networks: Embedding-Driven prediction of microbe-drug interactions.

Computers in biology and medicine
Drug repurposing accelerates microbial therapy development by bypassing the costly and time-consuming traditional drug discovery process. However, existing computational methods for predicting drug-microbe associations (MDAs) struggle to capture comp...

MRDDA: a multi-relational graph neural network for drug-disease association prediction.

Journal of translational medicine
BACKGROUND: Drug repositioning offers a promising avenue for accelerating drug development and reducing costs. Recently, computational repositioning approaches have gained attraction for identifying potential drug-disease associations (DDAs). Biologi...