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Drug Repositioning

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DeepDRK: a deep learning framework for drug repurposing through kernel-based multi-omics integration.

Briefings in bioinformatics
Recent pharmacogenomic studies that generate sequencing data coupled with pharmacological characteristics for patient-derived cancer cell lines led to large amounts of multi-omics data for precision cancer medicine. Among various obstacles hindering ...

Topological network measures for drug repositioning.

Briefings in bioinformatics
Drug repositioning has received increased attention since the past decade as several blockbuster drugs have come out of repositioning. Computational approaches are significantly contributing to these efforts, of which, network-based methods play a ke...

GraphDTA: predicting drug-target binding affinity with graph neural networks.

Bioinformatics (Oxford, England)
SUMMARY: The development of new drugs is costly, time consuming and often accompanied with safety issues. Drug repurposing can avoid the expensive and lengthy process of drug development by finding new uses for already approved drugs. In order to rep...

DTI-MLCD: predicting drug-target interactions using multi-label learning with community detection method.

Briefings in bioinformatics
Identifying drug-target interactions (DTIs) is an important step for drug discovery and drug repositioning. To reduce the experimental cost, a large number of computational approaches have been proposed for this task. The machine learning-based model...

Dr AFC: drug repositioning through anti-fibrosis characteristic.

Briefings in bioinformatics
Fibrosis is a key component in the pathogenic mechanism of a variety of diseases. These diseases involving fibrosis may share common mechanisms and therapeutic targets, and therefore common intervention strategies and medicines may be applicable for ...

Prediction of drug adverse events using deep learning in pharmaceutical discovery.

Briefings in bioinformatics
Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus ...

[Potential for Big Data Analysis Using AI in the Field of Clinical Pharmacy].

Yakugaku zasshi : Journal of the Pharmaceutical Society of Japan
Industrial reforms utilizing artificial intelligence (AI) have advanced remarkably in recent years. The application of AI to big data analysis in the medical information field has also been advancing and is expected to be used to find drug adverse ef...

Deep Learning in the Quest for Compound Nomination for Fighting COVID-19.

Current medicinal chemistry
The current COVID-19 pandemic initiated an unprecedented response from clinicians and the scientific community in all relevant biomedical fields. It created an incredible multidimensional data-rich framework in which deep learning proved instrumental...

Deep learning of pharmacogenomics resources: moving towards precision oncology.

Briefings in bioinformatics
The recent accumulation of cancer genomic data provides an opportunity to understand how a tumor's genomic characteristics can affect its responses to drugs. This field, called pharmacogenomics, is a key area in the development of precision oncology....

Application of machine intelligence technology in the detection of vaccines and medicines for SARS-CoV-2.

European review for medical and pharmacological sciences
Researchers have found many similarities between the 2003 severe acute respiratory syndrome (SARS) virus and SARS-CoV-19 through existing data that reveal the SARS's cause. Artificial intelligence (AI) learning models can be created to predict drug s...