Computational toxicology: an overview of the sources of data and of modelling methods

Nigsch, Florian; Macaluso, N. J. Maximilian; Mitchell, John B. O.; Zmuidinavicius, Donatas

doi:10.1517/17425250802660467

Cited by 68 publications

(29 citation statements)

References 97 publications

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“…this is a major difference to experimental approaches. An important consideration will thus be whether in silico methods are limited by the limitations of their input and whether we have any hope of overcoming their weaknesses or can only approximate them… there are some excellent introductions to and reviews of computational toxicology (Durham and Pearl, 2001;van de Waterbeemd, 2002;Greene, 2002;Veith, 2004, Helma, 2005Simon-Hettich et al, 2006;Kavlock et al, 2008;Merlot, 2008;Nigsch et al, 2009;Greene and Naven, 2009). In addition, the ex-eCB website (http://ecb.jrc.ec.europa.eu/qsar/), hosted by Andrew Worth and his team (chronically understaffed given the high expectations) who act as key promoters of computational toxicology, is an excellent resource.…”

Section: Food For Thought … On In Silico Methods In Toxicologymentioning

confidence: 99%

Food for thought … on in silico methods in toxicology

Hartung

2009

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this series of articles has already addressed in vitro and in vivo tools (Hartung, 2007;Hartung, 2008b). With this contribution addressing computational toxicology we now try to complete considerations on the main methodological approaches in toxicology. However, we do still plan to address specific aspects, such as endpoints (omics, image technologies), high-throughput testing or physiology-based pharmaco-(toxico-)kinetic modelling (PBPK), in later issues. All of these aspects have major in silico components, which already shows how difficult it is to discuss in silico methods on their own. Indeed, their integration and interplay with in vivo and in vitro approaches is critical, at least in the way their development often depends critically on the input of either in vitro or in vivo data. this is a major difference to experimental approaches. An important consideration will thus be whether in silico methods are limited by the limitations of their input and whether we have any hope of overcoming their weaknesses or can only approximate them… there are some excellent introductions to and reviews of

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Section: Food For Thought … On In Silico Methods In Toxicologymentioning

confidence: 99%

Food for thought … on in silico methods in toxicology

Hartung

2009

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“…The number and size of databases and available tools is steadily increasing (Nigsch et al, 2009;Rusyn and Daston, 2010;Raunio, 2011;Greene and Pennie, 2015). The prerequisite for making sense of these big data is that we deal with good big data.…”

Section: Grouping Of Substances and Read-acrossmentioning

confidence: 99%

Making big sense from big data in toxicology by read-across

Hartung

2016

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“…On average, the prediction accuracy with compounds with known targets is 77%. Bayesian classifier was usually used in early prediction, while the Winnow algorithm was reported more recently [77]. With the same training datasets, the prediction result is slightly different with the multiplecategory Laplacian model.…”

Section: Machine Learningmentioning

confidence: 99%

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications

Zhao

Wei

2014

Advances in Experimental Medicine and Biology

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The traditional Chinese medicine (TCM), which has thousands of years of clinical application among China and other Asian countries, is the pioneer of the "multicomponent-multitarget" and network pharmacology. Although there is no doubt of the efficacy, it is difficult to elucidate convincing underlying mechanism of TCM due to its complex composition and unclear pharmacology. The use of ligand-protein networks has been gaining significant value in the history of drug discovery while its application in TCM is still in its early stage. This paper firstly surveys TCM databases for virtual screening that have been greatly expanded in size and data diversity in recent years. On that basis, different screening methods and strategies for identifying active ingredients and targets of TCM are outlined based on the amount of network information available, both on sides of ligand bioactivity and the protein structures. Furthermore, applications of successful in silico target identification attempts are discussed in detail along with experiments in exploring the ligand-protein networks of TCM. Finally, it will be concluded that the prospective application of ligand-protein networks can be used not only to predict protein targets of a small molecule, but also to explore the mode of action of TCM.

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Computational toxicology: an overview of the sources of data and of modelling methods

Cited by 68 publications

References 97 publications

Food for thought … on in silico methods in toxicology

Food for thought … on in silico methods in toxicology

Making big sense from big data in toxicology by read-across

Exploring the Ligand-Protein Networks in Traditional Chinese Medicine: Current Databases, Methods and Applications

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