Literature DB >> 12924570

Structure-activity relationship approaches and applications.

Weida Tong1, William J Welsh, Leming Shi, Hong Fang, Roger Perkins.   

Abstract

New techniques and software have enabled ubiquitous use of structure-activity relationships (SARs) in the pharmaceutical industry and toxicological sciences. We review the status of SAR technology by using examples to underscore the advances as well as the unique technical challenges. Applying SAR involves two steps: Characterization of the chemicals under investigation, and application of chemometric approaches to explore data patterns or to establish the relationships between structure and activity. We describe generally but not exhaustively the SAR methodologies popular use in toxicology, including representation of chemical structure, and chemometric techniques where models are both unsupervised and supervised. The utility of SAR technology is most evident when supervised methods are used to predict toxicity of untested chemicals based only on chemical structure. Such models can predict on both an ordinal scale (e.g., active vs inactive) or a continuouis scale (e.g., median lethal dose [LD50] dose). The reader is also referred to a companion paper in this issue that discusses quantitative structure-activity relationship (QSAR) methods that have advanced markedly over the past decade.

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Mesh:

Year:  2003        PMID: 12924570     DOI: 10.1897/01-198

Source DB:  PubMed          Journal:  Environ Toxicol Chem        ISSN: 0730-7268            Impact factor:   3.742


  9 in total

1.  Computational structure-activity relationship analysis of small-molecule agonists for human formyl peptide receptors.

Authors:  Andrei I Khlebnikov; Igor A Schepetkin; Mark T Quinn
Journal:  Eur J Med Chem       Date:  2010-09-15       Impact factor: 6.514

2.  Toxicity testing in the 21st century: a vision and a strategy.

Authors:  Daniel Krewski; Daniel Acosta; Melvin Andersen; Henry Anderson; John C Bailar; Kim Boekelheide; Robert Brent; Gail Charnley; Vivian G Cheung; Sidney Green; Karl T Kelsey; Nancy I Kerkvliet; Abby A Li; Lawrence McCray; Otto Meyer; Reid D Patterson; William Pennie; Robert A Scala; Gina M Solomon; Martin Stephens; James Yager; Lauren Zeise
Journal:  J Toxicol Environ Health B Crit Rev       Date:  2010-02       Impact factor: 6.393

3.  Dynamic clustering threshold reduces conformer ensemble size while maintaining a biologically relevant ensemble.

Authors:  Austin B Yongye; Andreas Bender; Karina Martínez-Mayorga
Journal:  J Comput Aided Mol Des       Date:  2010-05-25       Impact factor: 3.686

Review 4.  Plant-Derived Natural Products as Lead Agents against Common Respiratory Diseases.

Authors:  Ayodeji Oluwabunmi Oriola; Adebola Omowunmi Oyedeji
Journal:  Molecules       Date:  2022-05-10       Impact factor: 4.927

5.  Structure-activity relationship analysis of N-benzoylpyrazoles for elastase inhibitory activity: a simplified approach using atom pair descriptors.

Authors:  Andrei I Khlebnikov; Igor A Schepetkin; Mark T Quinn
Journal:  Bioorg Med Chem       Date:  2008-01-15       Impact factor: 3.641

6.  Using decision forest to classify prostate cancer samples on the basis of SELDI-TOF MS data: assessing chance correlation and prediction confidence.

Authors:  Weida Tong; Qian Xie; Huixiao Hong; Leming Shi; Hong Fang; Roger Perkins; Emanuel F Petricoin
Journal:  Environ Health Perspect       Date:  2004-11       Impact factor: 9.031

Review 7.  Antioxidant Properties and Structure-Antioxidant Activity Relationship of Allium Species Leaves.

Authors:  Dikdik Kurnia; Dwipa Ajiati; Leny Heliawati; Dadan Sumiarsa
Journal:  Molecules       Date:  2021-11-26       Impact factor: 4.411

8.  Exploring the mode of action of inhibitors targeting the PhoP response regulator of Salmonella enterica through comprehensive pharmacophore approaches.

Authors:  Keng-Chang Tsai; Po-Pin Hung; Ching-Feng Cheng; Chinpan Chen; Tien-Sheng Tseng
Journal:  RSC Adv       Date:  2019-03-21       Impact factor: 3.361

9.  Assessment of prediction confidence and domain extrapolation of two structure-activity relationship models for predicting estrogen receptor binding activity.

Authors:  Weida Tong; Qian Xie; Huixiao Hong; Leming Shi; Hong Fang; Roger Perkins
Journal:  Environ Health Perspect       Date:  2004-08       Impact factor: 9.031

  9 in total

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