AIMC Topic: Drug Repositioning

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Next-generation drug repurposing using human genetics and network biology.

Current opinion in pharmacology
Drug repurposing has attracted increased attention, especially in the context of drug discovery rates that remain too low despite a recent wave of approvals for biological therapeutics (e.g. gene therapy). These new biological entities-based treatmen...

Machine and deep learning approaches for cancer drug repurposing.

Seminars in cancer biology
Knowledge of the underpinnings of cancer initiation, progression and metastasis has increased exponentially in recent years. Advanced "omics" coupled with machine learning and artificial intelligence (deep learning) methods have helped elucidate targ...

Old drug repositioning and new drug discovery through similarity learning from drug-target joint feature spaces.

BMC bioinformatics
BACKGROUND: Detection of new drug-target interactions by computational algorithms is of crucial value to both old drug repositioning and new drug discovery. Existing machine-learning methods rely only on experimentally validated drug-target interacti...

Time-resolved evaluation of compound repositioning predictions on a text-mined knowledge network.

BMC bioinformatics
BACKGROUND: Computational compound repositioning has the potential for identifying new uses for existing drugs, and new algorithms and data source aggregation strategies provide ever-improving results via in silico metrics. However, even with these a...

Identification of repurposable drugs with beneficial effects on glucose control in type 2 diabetes using machine learning.

Pharmacology research & perspectives
Despite effective medications, rates of uncontrolled glucose levels in type 2 diabetes remain high. We aimed to test the utility of machine learning applied to big data in identifying the potential role of concomitant drugs not taken for diabetes whi...

A Bayesian machine learning approach for drug target identification using diverse data types.

Nature communications
Drug target identification is a crucial step in development, yet is also among the most complex. To address this, we develop BANDIT, a Bayesian machine-learning approach that integrates multiple data types to predict drug binding targets. Integrating...

The assessment of efficient representation of drug features using deep learning for drug repositioning.

BMC bioinformatics
BACKGROUND: De novo drug discovery is a time-consuming and expensive process. Nowadays, drug repositioning is utilized as a common strategy to discover a new drug indication for existing drugs. This strategy is mostly used in cases with a limited num...

Rethinking Drug Repositioning and Development with Artificial Intelligence, Machine Learning, and Omics.

Omics : a journal of integrative biology
Pharmaceutical industry and the art and science of drug development are sorely in need of novel transformative technologies in the current age of digital health and artificial intelligence (AI). Often described as game-changing technologies, AI and m...

Analysis of defective pathways and drug repositioning in Multiple Sclerosis via machine learning approaches.

Computers in biology and medicine
BACKGROUND: Although some studies show that there could be a genetic predisposition to develop Multiple Sclerosis (MS), attempts to find genetic signatures related to MS diagnosis and development are extremely rare.

EK-DRD: A Comprehensive Database for Drug Repositioning Inspired by Experimental Knowledge.

Journal of chemical information and modeling
Drug repositioning, or the identification of new indications for approved therapeutic drugs, has gained substantial traction with both academics and pharmaceutical companies because it reduces the cost and duration of the drug development pipeline an...