Introduction to Cheminformatics

Wishart, David S.

doi:10.1002/0471250953.bi1401s18

Cited by 31 publications

(18 citation statements)

References 48 publications

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“…1, please see also references [47][48][49][50][51][52][53][54][55][56][57][58]. Non-targeted assays result in signal patterns ('fingerprints') that are analyzed using chemometric methods [48,50,51]. Chemometrics involve statistical pattern recognition models and detection of molecular markers from diverse, highly dimensional omics datasets.…”

Section: Discoverymentioning

confidence: 99%

“…In this context, the complexity and heterogeneity of metabolites are considerably greater than those of genes and proteins. These problems have been addressed by the development of algorithms for data reduction and filtering, for false discovery rate control, and for high‐dimensional statistical modeling strategies . However, it is impossible to completely exclude that the selection of the chemometric/bioinformatics strategy may affect the results and conclusions.…”

Section: Challengesmentioning

confidence: 99%

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Proteomics and metabolomics in renal transplantation-quo vadis?

et al. 2012

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The improvement of long-term transplant organ and patient survival remains a critical challenge following kidney transplantation. Proteomics and biochemical profiling (metabolomics) may allow for the detection of early changes in cell signal transduction regulation and biochemistry with high sensitivity and specificity. Hence, these analytical strategies hold the promise to detect and monitor disease processes and drug effects before histopathological and pathophysiological changes occur. In addition they will identify enriched populations and enable individualize drug therapy. However, proteomics and metabolomics have not yet lived up to such high expectations. Renal transplant patients are highly complex, making it difficult to establish cause-effect relationships between surrogate markers and disease processes. Appropriate study design, adequate sample handling, storage and processing, quality and reproducibility of bioanalytical multi-analyte assays, data analysis and interpretation, mechanistic verification and clinical qualification (=establishment of sensitivity and specificity in adequately powered prospective clinical trials) are important factors for the success of molecular marker discovery and development in renal transplantation. However, a newly developed and appropriately qualified molecular marker can only be successful if it is realistic that it can be implemented in a clinical setting. The development of combinatorial markers with supporting software tools is an attractive goal.

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Section: Discoverymentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Proteomics and metabolomics in renal transplantation-quo vadis?

et al. 2012

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“…How does bioinformatics contribute to phenotypic screening? The answer lies in the fact that many modern phenotypic screening studies, especially in screening for anti-cancer drugs, typically define phenotype, either implicitly or explicitly, as a gene expression (transcripts or protein) profile [11] or a metabolomic profile [135][136][137]. From this perspective, there are two alternative approaches to treat cancer cells.…”

Section: Phenotypic Screeningmentioning

confidence: 99%

Bioinformatics and Drug Discovery

Theiss¹

2019

Methods in Molecular Biology

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Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanismbased drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.

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“…How does bioinformatics contribute to phenotypic screening? The answer lies in the fact that many modern phenotypic screening studies, especially in screening for anti-cancer drugs, typically define phenotype, either implicitly or explicitly, as a gene expression (transcripts or protein) profile [ 11 ] or a metabolomic profile [ 135 - 137 ]. From this perspective, there are two alternative approaches to treat cancer cells.…”

Section: Transcriptomics and Drug Discoverymentioning

confidence: 99%

Bioinformatics and Drug Discovery

Xia

2017

CTMC

173

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Bioinformatic analysis can not only accelerate drug target identification and drug candidate screening and refinement, but also facilitate characterization of side effects and predict drug resistance. High-throughput data such as genomic, epigenetic, genome architecture, cistromic, transcriptomic, proteomic, and ribosome profiling data have all made significant contribution to mechanism-based drug discovery and drug repurposing. Accumulation of protein and RNA structures, as well as development of homology modeling and protein structure simulation, coupled with large structure databases of small molecules and metabolites, paved the way for more realistic protein-ligand docking experiments and more informative virtual screening. I present the conceptual framework that drives the collection of these high-throughput data, summarize the utility and potential of mining these data in drug discovery, outline a few inherent limitations in data and software mining these data, point out news ways to refine analysis of these diverse types of data, and highlight commonly used software and databases relevant to drug discovery.

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Introduction to Cheminformatics

Cited by 31 publications

References 48 publications

Proteomics and metabolomics in renal transplantation-quo vadis?

Proteomics and metabolomics in renal transplantation-quo vadis?

Bioinformatics and Drug Discovery

Bioinformatics and Drug Discovery

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