Computational polypharmacology comes of age

Rastelli, Giulio; Pinzi, Luca

doi:10.3389/fphar.2015.00157

Cited by 63 publications

(55 citation statements)

References 16 publications

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“…Structure-based methods, ligand-based approaches, QSAR or docking simulation and deep learning are well documented virtual screening technologies [25,26]. The Connectivity map (CMAP) established the first collection for genome-wide transcriptional expression data from small-molecule-treated human cells and simple pattern-matching algorithms [27].…”

Section: Computational Approach For Polypharmacologymentioning

confidence: 99%

Drug combination therapy increases successful drug repositioning

Sun

Sanderson

Zheng

2016

Drug Discovery Today

348

210

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Repositioning of approved drugs has recently gained new momentum for rapid identification and development of new therapeutics for diseases that lack effective drug treatment. Reported repurposing screens have increased dramatically in number in the past five years. However, many newly identified compounds have low potency; this limits their immediate clinical applications because the known, tolerated plasma drug concentrations are lower than the required therapeutic drug concentrations. Drug combinations of two or more compounds with different mechanisms of action are an alternative approach to increase the success rate of drug repositioning.

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Section: Computational Approach For Polypharmacologymentioning

confidence: 99%

Drug combination therapy increases successful drug repositioning

Sun

Sanderson

Zheng

2016

Drug Discovery Today

348

210

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“…Numerous methods have been developed to predict compound polypharmacology . Prediction of the binding affinity of ligands to multiple proteins allows to anticipate potential selectivity issues, discover beneficial multi‐target activities as early as possible in the drug discovery process, or make target deconvolution for phenotypic screening . Most of these methods rely on building single target model individually, one future development could be to use all available chemogenomics data to pursue multi‐task learning and build one multi‐label model to predict multiple target activity simultaneously.…”

Section: Structure‐activity Relationship Modelingmentioning

confidence: 99%

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

Tetko

Engkvist

Koch

et al. 2016

Molecular Informatics

105

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The increasing volume of biomedical data in chemistry and life sciences requires the development of new methods and approaches for their handling. Here, we briefly discuss some challenges and opportunities of this fast growing area of research with a focus on those to be addressed within the BIGCHEM project. The article starts with a brief description of some available resources for “Big Data” in chemistry and a discussion of the importance of data quality. We then discuss challenges with visualization of millions of compounds by combining chemical and biological data, the expectations from mining the “Big Data” using advanced machine‐learning methods, and their applications in polypharmacology prediction and target de‐convolution in phenotypic screening. We show that the efficient exploration of billions of molecules requires the development of smart strategies. We also address the issue of secure information sharing without disclosing chemical structures, which is critical to enable bi‐party or multi‐party data sharing. Data sharing is important in the context of the recent trend of “open innovation” in pharmaceutical industry, which has led to not only more information sharing among academics and pharma industries but also the so‐called “precompetitive” collaboration between pharma companies. At the end we highlight the importance of education in “Big Data” for further progress of this area.

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“…Indeed, computational approaches have certainly proved to play a key role in exploiting the available structural information, and to perform de novo multi-target drug design and in silico profiling [8]. Moreover, a multitude of molecular modeling methods, which can be broadly classified into structure-and ligand-based approaches, are currently available to aid in polypharmacology drug design [9,10].…”

Section: Introductionmentioning

confidence: 99%

Selection of protein conformations for structure-based polypharmacology studies

Pinzi

Caporuscio

Rastelli

2018

Drug Discovery Today

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Several drugs exert their therapeutic effect through the modulation of multiple targets. Structure-based approaches hold great promise for identifying compounds with the desired polypharmacological profiles. These methods use knowledge of the protein binding sites to identify stereoelectronically complementary ligands. The selection of the most suitable protein conformations to be used in the design process is vital, especially for multitarget drug design in which the same ligand has to be accommodated in multiple binding pockets. Herein, we focus on currently available techniques for the selection of the most suitable protein conformations for multitarget drug design, compare the potential advantages and limitations of each method, and comment on how their combination could help in polypharmacology drug design.

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Computational polypharmacology comes of age

Abstract: No abstrac

Cited by 63 publications

References 16 publications

Drug combination therapy increases successful drug repositioning

Drug combination therapy increases successful drug repositioning

BIGCHEM: Challenges and Opportunities for Big Data Analysis in Chemistry

Selection of protein conformations for structure-based polypharmacology studies

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