The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

Katritzky, Alan R.; Fara, Dan C.; Petrukhin, Ruslan; Tatham, Douglas B.; Maran, Uko; Lomaka, Andre; Karelson, Mati

doi:10.2174/1568026023392922

Cited by 73 publications

(37 citation statements)

References 180 publications

(70 reference statements)

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“…This procedure has become very popular for prediction of many other physicochemical parameters of compounds and their biological activity [150,[176][177][178][179][180][181][182][183][184][185][186][187][188]. Extensive use of QSPR in solving various problems of physical organic chemistry is favored by development of computational methods and instruments and statistical processing procedures such as principal component analysis (PCA) [154,[163][164][165]185], independent component analysis (ICA) [189], partial least squares (PLS) [153,154,156,157,168,174] in combination with iterative variable elimination (IVE-PLS) [156], multilinear regression (MLR) [155,157,159,160,175,187], and variable importance in projection (VIP) [190,191].…”

Section: Methods Based On Quantitative Structure-property Relationshimentioning

confidence: 99%

Modern methods for estimation of ionization constants of organic compounds in solution

Zevatskii

Samoilov²

2011

Russ J Org Chem

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The review considers main up-to-date approaches to the calculation of ionization constants of organic compounds in solutions. Methods based on quantum-chemical calculations, continuum solvent models, cluster models, and quantitative structure-property relationships are discussed. The results of calculations of pK a values by different methods, obtained in the past decade, are compared. REVIEW ACID IONIZATION CONSTANTS AND THEIR SIGNIFICANCEA quantitative parameter of acid-base properties of an HA molecule is the equilibrium constant for proton transfer (K a ) from acid HA to solvent molecule M with formation of lyonium ion and acid anion [1-3] (if an acid is an onium ion, neutral base is formed).

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Section: Methods Based On Quantitative Structure-property Relationshimentioning

confidence: 99%

Modern methods for estimation of ionization constants of organic compounds in solution

Zevatskii

Samoilov²

2011

Russ J Org Chem

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“…QSAR analysis has been used mainly in the fields of medicine, pharmacy, environmental science, toxicology, and biology as a way of predicting physico-chemical and biological properties via statistical models, and its use has gradually spread to other areas. 13,[16][17][18] Additionally, QSAR analyses are expected to be valued by national and international organizations from a chemical management perspective, in that toxicity estimates generated by QSAR are allowed in REACH (registration, evaluation, and authorization of chemicals). In particular, hazard estimations in the context of occupational health are needed.…”

Section: -12mentioning

confidence: 99%

Prediction of the Toxicity of Dimethylformamide, Methyl Ethyl Ketone, and Toluene Mixtures by QSAR Modeling

Kim¹,

Won²,

Hong³

et al. 2014

Bulletin of the Korean Chemical Society

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In this study, we analyzed the toxicity of mixtures of dimethylformamide (DMF) and methyl ethyl ketone (MEK) or DMF and toluene (TOL) and predicted their toxicity using quantitative structure-activity relationships (QSAR). A QSAR model for single substances and mixtures was analyzed using multiple linear regression (MLR) by taking into account the statistical parameters between the observed and predicted EC 50 . After preprocessing, the best subsets of descriptors in the learning methods were determined using a 5-fold cross-validation method. Significant differences in physico-chemical properties such as boiling point (BP), specific gravity (SG), Reid vapor pressure (rVP), flash point (FP), low explosion limit (LEL), and octanol/ water partition coefficient (Pow) were observed between the single substances and the mixtures. The EC 50 of the mixture of DMF and TOL was significantly lower than that of DMF. The mixture toxicity was directly related to the mixing ratio of TOL and MEK (MLR EC 50 equation = 1.76997 − 1.12249 × TOL + 1.21045 × MEK), as well as to SG, VP, and LEL (MLR equation EC 50 = 15.44388 − 19.84549 × SG + 0.05091 × VP + 1.85846 × LEL). These results show that QSAR-based models can be used to quantitatively predict the toxicity of mixtures used in manufacturing industries.

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“…The reliable link between chemical structures and chemical properties facilitates research into algorithms and techniques which operate on these structural representations and produce reliable predictions of properties [18]–[20]. This allows, among other applications, computational screening , which is the pre-selection of interesting structures for given purposes from the large chemical libraries.…”

Section: Introductionmentioning

confidence: 99%

The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web

et al. 2011

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Cheminformatics is the application of informatics techniques to solve chemical problems in silico. There are many areas in biology where cheminformatics plays an important role in computational research, including metabolism, proteomics, and systems biology. One critical aspect in the application of cheminformatics in these fields is the accurate exchange of data, which is increasingly accomplished through the use of ontologies. Ontologies are formal representations of objects and their properties using a logic-based ontology language. Many such ontologies are currently being developed to represent objects across all the domains of science. Ontologies enable the definition, classification, and support for querying objects in a particular domain, enabling intelligent computer applications to be built which support the work of scientists both within the domain of interest and across interrelated neighbouring domains. Modern chemical research relies on computational techniques to filter and organise data to maximise research productivity. The objects which are manipulated in these algorithms and procedures, as well as the algorithms and procedures themselves, enjoy a kind of virtual life within computers. We will call these information entities. Here, we describe our work in developing an ontology of chemical information entities, with a primary focus on data-driven research and the integration of calculated properties (descriptors) of chemical entities within a semantic web context. Our ontology distinguishes algorithmic, or procedural information from declarative, or factual information, and renders of particular importance the annotation of provenance to calculated data. The Chemical Information Ontology is being developed as an open collaborative project. More details, together with a downloadable OWL file, are available at http://code.google.com/p/semanticchemistry/ (license: CC-BY-SA).

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The Present Utility and Future Potential for Medicinal Chemistry of QSAR / QSPR with Whole Molecule Descriptors

Abstract: Whole-molecule descriptors are obtained computationally from molecular structures using a variety of programs. Their applications are reviewed in the areas of solubility, bioavailability, bio-and nonbio-degradability and toxicity.

Cited by 73 publications

References 180 publications

Modern methods for estimation of ionization constants of organic compounds in solution

Modern methods for estimation of ionization constants of organic compounds in solution

Prediction of the Toxicity of Dimethylformamide, Methyl Ethyl Ketone, and Toluene Mixtures by QSAR Modeling

The Chemical Information Ontology: Provenance and Disambiguation for Chemical Data on the Biological Semantic Web

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