AIMC Topic: Drug Repositioning

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Prediction of drug efficacy from transcriptional profiles with deep learning.

Nature biotechnology
Drug discovery focused on target proteins has been a successful strategy, but many diseases and biological processes lack obvious targets to enable such approaches. Here, to overcome this challenge, we describe a deep learning-based efficacy predicti...

In silico drug repositioning using deep learning and comprehensive similarity measures.

BMC bioinformatics
BACKGROUND: Drug repositioning, meanings finding new uses for existing drugs, which can accelerate the processing of new drugs research and development. Various computational methods have been presented to predict novel drug-disease associations for ...

Machine learning techniques applied to the drug design and discovery of new antivirals: a brief look over the past decade.

Expert opinion on drug discovery
: Drug design and discovery of new antivirals will always be extremely important in medicinal chemistry, taking into account known and new viral diseases that are yet to come. Although machine learning (ML) have shown to improve predictions on the bi...

Drug repurposing for hyperlipidemia associated disorders: An integrative network biology and machine learning approach.

Computational biology and chemistry
Hyperlipidemia causes diseases like cardiovascular disease, cancer, Type II Diabetes and Alzheimer's disease. Drugs that specifically target HL associated diseases are required for treatment. 34 KEGG pathways targeted by lipid lowering drugs were use...

CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.

Biomolecules
The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, w...

Explainable artificial intelligence in high-throughput drug repositioning for subgroup stratifications with interventionable potential.

Journal of biomedical informatics
Enabling precision medicine requires developing robust patient stratification methods as well as drugs tailored to homogeneous subgroups of patients from a heterogeneous population. Developing de novo drugs is expensive and time consuming with an ult...

Deep-learning based repurposing of FDA-approved drugs against dihydrofolate reductase and molecular dynamics study.

Journal of biomolecular structure & dynamics
causes the fatal fungal bloodstream infection in humans called Candidiasis. Most of the species are resistant to the antifungals used to treat them. Drug-resistant poses very serious public health issues. To overcome this, the development of effec...

Knowledge graphs and their applications in drug discovery.

Expert opinion on drug discovery
INTRODUCTION: Knowledge graphs have proven to be promising systems of information storage and retrieval. Due to the recent explosion of heterogeneous multimodal data sources generated in the biomedical domain, and an industry shift toward a systems b...

An Analytical Review of Computational Drug Repurposing.

IEEE/ACM transactions on computational biology and bioinformatics
Drug repurposing is a vital function in pharmaceutical fields and has gained popularity in recent years in both the pharmaceutical industry and research community. It refers to the process of discovering new uses and indications for existing or faile...

A Novel Drug Repositioning Approach Based on Collaborative Metric Learning.

IEEE/ACM transactions on computational biology and bioinformatics
Computational drug repositioning, which is an efficient approach to find potential indications for drugs, has been used to increase the efficiency of drug development. The drug repositioning problem essentially is a top-K recommendation task that rec...