Drugs, their targets and the nature and number of drug targets

Imming, Peter; Sinning, Christian; Meyer, A. B.

doi:10.1038/nrd2132

Cited by 753 publications

(472 citation statements)

References 257 publications

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“…TFs are generally considered to be poor drug targets due to the inability of small molecules to block protein-protein and protein-DNA binding interfaces (Imming et al 2006). Although chemical genomics provides examples of small molecules that can modulate the activity of TFs, until now, few small molecules were reported to directly bind and inhibit TFs.…”

Section: Therapeutic Implicationsmentioning

confidence: 99%

Physiological/pathological ramifications of transcription factors in the unfolded protein response

2017

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Numerous environmental, physiological, and pathological insults disrupt protein-folding homeostasis in the endoplasmic reticulum (ER), referred to as ER stress. Eukaryotic cells evolved a set of intracellular signaling pathways, collectively termed the unfolded protein response (UPR), to maintain a productive ER protein-folding environment through reprogramming gene transcription and mRNA translation. The UPR is largely dependent on transcription factors (TFs) that modulate expression of genes involved in many physiological and pathological conditions, including development, metabolism, inflammation, neurodegenerative diseases, and cancer. Here we summarize the current knowledge about these mechanisms, their impact on physiological/pathological processes, and potential therapeutic applications.

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Section: Therapeutic Implicationsmentioning

confidence: 99%

Physiological/pathological ramifications of transcription factors in the unfolded protein response

2017

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“…Niclosamide was developed on the basis of activity in animal models of parasitic worm infection without knowledge of its target(s), and its mechanism of action remains poorly defined (51). Niclosamide is believed to owe its antiparasitic activity to its ability to disrupt the pH homeostasis of the parasite (52).…”

Section: Mammalian Target Of Rapamycin (Mtor)mentioning

confidence: 99%

Structure-Activity Analysis of Niclosamide Reveals Potential Role for Cytoplasmic pH in Control of Mammalian Target of Rapamycin Complex 1 (mTORC1) Signaling

Fonseca

Diering

Bidinosti

et al. 2012

Journal of Biological Chemistry

146

122

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Background: mTORC1 is dysregulated in human disease, and there is an interest in the development of mTORC1 inhibitors. Niclosamide inhibits mTORC1 signaling, but its mode of action remains unclear. Results: Niclosamide extrudes protons from lysosomes, thus lowering cytoplasmic pH and inhibiting mTORC1 signaling. Conclusion: Cytoplasmic acidification inhibits mTORC1 signaling. Significance: Our findings may aid the design of niclosamide-based anticancer therapeutic agents.

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“…We also used drug target groupings (Imming et al 2006), such as 'drug target classes', and 'the preferred name groupings', as meta-features. These enable meta-learning to exploit known biological/chemical relationships between the targets (proteins).…”

Section: Drug Target Groupingsmentioning

confidence: 99%

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

et al. 2017

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We investigate the learning of quantitative structure activity relationships (QSARs) as a case-study of meta-learning. This application area is of the highest societal importance, as it is a key step in the development of new medicines. The standard QSAR learning problem Learn (2018) 107:285-311 is: given a target (usually a protein) and a set of chemical compounds (small molecules) with associated bioactivities (e.g. inhibition of the target), learn a predictive mapping from molecular representation to activity. Although almost every type of machine learning method has been applied to QSAR learning there is no agreed single best way of learning QSARs, and therefore the problem area is well-suited to meta-learning. We first carried out the most comprehensive ever comparison of machine learning methods for QSAR learning: 18 regression methods, 3 molecular representations, applied to more than 2700 QSAR problems. (These results have been made publicly available on OpenML and represent a valuable resource for testing novel meta-learning methods.) We then investigated the utility of algorithm selection for QSAR problems. We found that this meta-learning approach outperformed the best individual QSAR learning method (random forests using a molecular fingerprint representation) by up to 13%, on average. We conclude that meta-learning outperforms base-learning methods for QSAR learning, and as this investigation is one of the most extensive ever comparisons of base and meta-learning methods ever made, it provides evidence for the general effectiveness of meta-learning over base-learning.

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Drugs, their targets and the nature and number of drug targets

Abstract: What is a drug target? And how many such targets are there? Here, we consider the nature of drug targets, and by classifying known drug substances on the basis of the discussed principles we provide an estimation of the total number of current drug targets.

Cited by 753 publications

References 257 publications

Physiological/pathological ramifications of transcription factors in the unfolded protein response

Physiological/pathological ramifications of transcription factors in the unfolded protein response

Structure-Activity Analysis of Niclosamide Reveals Potential Role for Cytoplasmic pH in Control of Mammalian Target of Rapamycin Complex 1 (mTORC1) Signaling

Meta-QSAR: a large-scale application of meta-learning to drug design and discovery

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