Identification of drug candidates and repurposing opportunities through compound–target interaction networks

@article{Cichoska2015IdentificationOD,
  title={Identification of drug candidates and repurposing opportunities through compound–target interaction networks},
  author={Anna Cichońska and Juho Rousu and Tero A Aittokallio},
  journal={Expert Opinion on Drug Discovery},
  year={2015},
  volume={10},
  pages={1333 - 1345}
}
Introduction: System-wide identification of both on- and off-targets of chemical probes provides improved understanding of their therapeutic potential and possible adverse effects, thereby accelerating and de-risking drug discovery process. Given the high costs of experimental profiling of the complete target space of drug-like compounds, computational models offer systematic means for guiding these mapping efforts. These models suggest the most potent interactions for further experimental or… 
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References

SHOWING 1-10 OF 103 REFERENCES
Discovery of drug mode of action and drug repositioning from transcriptional responses
TLDR
This work developed an automatic and robust approach that exploits similarity in gene expression profiles following drug treatment, across multiple cell lines and dosages, to predict similarities in drug effect and MoA, and correctly predicted the MoA for nine anticancer compounds and was able to discover an unreported effect for a well-known drug.
Drug–target interaction prediction via chemogenomic space: learning-based methods
TLDR
In spite of many improvements for pharmacology applications by learning-based methods, there are many over simplification settings in construction of predictive models that may lead to over-optimistic results on drug–target interaction prediction.
Network approaches to drug discovery
TLDR
By reconstructing networks of targets, drugs and drug candidates as well as gene expression profiles under normal and disease conditions, the paper illustrates how it is possible to find relationships between different diseases, find biomarkers, explore drug repurposing and study emergence of drug resistance.
Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products.
TLDR
It is argued that a network pharmacology approach would enable an effective mapping of the yet unexplored target space of natural products, hence providing a systematic means to extend the druggable space of proteins implicated in various complex diseases.
Toward more realistic drug–target interaction predictions
A number of supervised machine learning models have recently been introduced for the prediction of drug–target interactions based on chemical structure and genomic sequence information. Although
Network-Based Relating Pharmacological and Genomic Spaces for Drug Target Identification
TLDR
The findings demonstrate that the integration of phenotypic and chemical indexes in pharmacological space and protein-protein interactions in genomic space can not only speed the genome-wide identification of drug targets but also find new applications for the existing drugs.
Network pharmacology: the next paradigm in drug discovery.
TLDR
A new appreciation of the role of polypharmacology has significant implications for tackling the two major sources of attrition in drug development--efficacy and toxicity.
Network Pharmacology Strategies Toward Multi-Target Anticancer Therapies: From Computational Models to Experimental Design Principles
TLDR
A specific focus is on system-level network approaches to polypharmacology designs in anticancer drug discovery, where representative examples of how network-centric modeling may offer systematic strategies toward better understanding and even predicting the phenotypic responses to multi-target therapies are given.
Drug Target Identification Using Side-Effect Similarity
TLDR
Applied to 746 marketed drugs, a network of 1018 side effect–driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications, hinting at new uses of marketed drugs.
Finding the targets of a drug by integration of gene expression data with a protein interaction network.
TLDR
A network-based computational method for drug target prediction, applicable on a genome-wide scale, relies on the analysis of gene expression following drug treatment in the context of a functional protein association network and indicates the predictive power of integrating experimental gene expression data with prior knowledge from protein association networks.
...
1
2
3
4
5
...