The History and Development of Quantitative Structure-Activity Relationships (QSARs)

Dearden, John C.

doi:10.4018/ijqspr.2016010101

Cited by 112 publications

(74 citation statements)

References 229 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Therefore, it is necessary to develop mathematical models to predict the various property endpoints of ILs. Quantitative structure-property/activity relationship (QSPR/QSAR) methods [7][8][9] would help to develop quantitative models capable of predicting the properties directly from molecular structure information.…”

Section: Introductionmentioning

confidence: 99%

Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests

et al. 2016

View full text Add to dashboard Cite

Despite possessing an interesting chemical nature and tuneable physicochemical properties, ionic liquids (ILs) must have their ecotoxicity tested in order to be commercialized. The water solubility of ILs allows their easy access to the aquatic compartment of the ecosystem creating a potential hazard to aquatic organisms. Hence, it is relevant to design ionic liquids with lower toxicity while keeping the desired properties of interest. Considering the possibility of an enormous number of combinations of different cations and anions, a rational guidance for the structural design of ionic liquids is essential in order to prioritize the synthesis as well as testing of selected molecules only. Predictive in silico models, such as quantitative structure-activity relationship (QSAR) models, can play a pivotal role in exploring the important chemical attributes contributing to the response activity. These models may then lead to the design of novel ionic liquids. The present study aims at developing predictive QSAR models for the ecotoxicity of ionic liquids using the bacteria Vibrio fischeri as an indicator response species. Instead of a single model, here we have used multiple models to capture more complete structural information of ionic liquids for toxicity towards Vibrio fischeri. The derived chemical attributes have been implemented in designing new analogues, some of which have been synthesized and had their ecotoxicity tested for the same model organism. The predictive QSAR models reported here can be used for ecotoxicity prediction of new IL chemicals and for data-gap filling. Moreover, the synthesized low-toxic ILs could be considered for evaluation as well as for application in suitable processes serving the purpose of industry and academia.

show abstract

Section: Introductionmentioning

confidence: 99%

Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests

et al. 2016

View full text Add to dashboard Cite

show abstract

“…At the present different families of structures have been suggested as HER2 inhibitors and their efficiencies reported . The QSAR statistical approach, which correlates quantitative response activities with numerical descriptors from a set of training molecules, has proved to be an essential technique in the discovery of new drugs . Trustworthy models can improve drastically the proficiency and pace of detection of more effective drugs with weaker secondary effects.…”

Section: Introductionmentioning

confidence: 99%

Conformation‐Independent QSAR Study on Human Epidermal Growth Factor Receptor‐2 (HER2) Inhibitors

et al. 2017

View full text Add to dashboard Cite

Inhibition of HER2 (human epidermal growth factor receptor 2) expression and function is required in several cancer treatments. Numerous compounds with very different molecular structures have been suggested as HER2 inhibitors. Here we perform quantitative structure‐activity relationship (QSAR) analysis on 444 of such compounds to investigate the molecular properties that may influence its efficiency. Models based on 1D and 2D flexible molecular descriptors are proposed to develop simple models based solely on constitutional and topological molecular features. A large number of non‐conformational descriptors (17974) was used to thoroughly explore the structural characteristics that influence the HER2 inhibitory activity. Three different approaches were explored using: 1) Molecular Descriptors, 2) Flexible Molecular Descriptors, and 3) Hybrid Descriptors. A QSAR model for HER2 inhibitors was successfully developed. Some properties such as electronegativity, aromatic character, and the presence of amino groups appear as molecular characteristics that may have influence in the HER2 inhibitory activity.

show abstract

“…9 It has even been claimed 2 that a car was seen on a U.S. highway with a bumper sticker reading: "Corwin Hansch walks on octanol". The growth of QSAR publications continues apace, 2 with over 1500 QSAR publications in both 2015 and 2016. At the same time, the range and complexity of techniques is also steadily increasing -to the concern, it has to be said, of many practitioners, since QSAR requires human expertise as well as computational proficiency.…”

Section: School Of Pharmacy and Biomolecular Sciences Liverpool John Mmentioning

confidence: 99%

“…8 His QSARs for narcotic effects of chemicals on animals are virtually indistinguishable from those currently being reported. Of course the work of Corwin Hansch and his coworkers is synonymous with the growth and development of modern QSAR, 2 and it is not for nothing that Hansch has been called the father of QSAR. 9 It has even been claimed 2 that a car was seen on a U.S. highway with a bumper sticker reading: "Corwin Hansch walks on octanol".…”

Section: School Of Pharmacy and Biomolecular Sciences Liverpool John Mmentioning

confidence: 99%

Whither QSAR?

Dearden¹

2017

Pharm Sci

View full text Add to dashboard Cite

To see the future, we must look at the past (German proverb). A QSAR (quantitative structure-activity relationship) is a mathematical equation that relates a property of interest such as a biological potency with one or more molecular properties (called descriptors), for a series of (usually) related compounds. A good example is the correlation of the anti-inflammatory potency of a series of ringsubstituted aspirin derivatives in the rat: In equation 1, ED50 = dose required to reduce paw inflammation by 50%, P = octanol-water partition coefficient, n = number of compounds used to develop the QSAR, r 2 =coefficient of determination, and s = standard error of the model. The statistics are very good, for r 2 = 0.904 means that the QSAR model accounts for 90.4% of the variation of anti-inflammatory potency. The use of log P as sole descriptor suggests that penetration of each compound to the site of action is the controlling factor. I wonder how many hundreds of aspirin derivatives were made and tested in years gone by in the hope of finding "a better aspirin", when the above model indicates that the best possible potency (assuming the same mode of action) would be only about 1.75 times that of aspirin. In line with the sentiment expressed in the German proverb that heads this Editorial, I have recently published two papers dealing with the history of QSAR.2,3 I was amazed to discover how much early work had been done. The earliest quantitative property prediction that I could find is that of Döbereiner, who in 1816 accurately predicted the specific weight of strontium sulphate by interpolation (read-across, in modern parlance) from those of calcium and barium sulphates. Even more amazing, in a way, is the insight shown by the Roman poet and philosopher Titus Lucretius Carus (ca. 99-55 B.C.), who in his De Rerum Natura 5 wrote: We see how quickly through a colander The wines will flow; how, on the other hand,

show abstract

The History and Development of Quantitative Structure-Activity Relationships (QSARs)

Cited by 112 publications

References 229 publications

Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests

Development of predictive QSAR models for Vibrio fischeri toxicity of ionic liquids and their true external and experimental validation tests

Conformation‐Independent QSAR Study on Human Epidermal Growth Factor Receptor‐2 (HER2) Inhibitors

Whither QSAR?

Contact Info

Product

Resources

About