The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure‐Property Modeling

Hall, Lowell H.; Kier, Lemont B.

doi:10.1002/9780470125793.ch9

Cited by 318 publications

(172 citation statements)

References 120 publications

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“…A number of topological descriptors were calculated, but only those displaying values with considerable difference between active and inactive molecules were used further. These included the Chi0V, Chi1N, Kappa1 and HallKierAlpha descriptors (Hall and Kier, 1991) as well as the topological polar surface area. The descriptors were transformed into a binary vector by binning.…”

Section: Calculation and Combination Of Fingerprintsmentioning

confidence: 99%

Molecular similarity-based predictions of the Tox21 screening outcome

Drwal

Siramshetty

Banerjee

et al. 2015

Front. Environ. Sci.

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Citation:Drwal MN, Siramshetty VB, Banerjee P, Goede A, Preissner R and Dunkel M (2015) To assess the toxicity of new chemicals and drugs, regulatory agencies require in vivo testing for many toxic endpoints, resulting in millions of animal experiments conducted each year. However, following the Replace, Reduce, Refine (3R) principle, the development and optimization of alternative methods, in particular in silico methods, has been put into focus in the recent years. It is generally acknowledged that the more complex a toxic endpoint, the more difficult it is to model. Therefore, computational toxicology is shifting from modeling general and complex endpoints to the investigation and modeling of pathways of toxicity and the underlying molecular effects. The U.S. Toxicology in the twenty-first century (Tox21) initiative has screened a large library of compounds, including approximately 10K environmental chemicals and drugs, for different mechanisms responsible for eliciting toxic effects, and made the results publicly available. Through the Tox21 Data Challenge, the consortium has established a platform for computational toxicologists to develop and validate their predictive models. Here, we present a fast and successful method for the prediction of different outcomes of the nuclear receptor and stress response pathway screening from the Tox21 Data Challenge 2014. The method is based on the combination of molecular similarity calculations and a naïve Bayes machine learning algorithm and has been implemented as a KNIME pipeline. Molecules are represented as binary vectors consisting of a concatenation of common two-dimensional molecular fingerprint types with topological compound properties. The prediction method has been optimized individually for each modeled target and evaluated in a cross-validation as well as with the independent Tox21 validation set. Our results show that the method can achieve good prediction accuracies and rank among the top algorithms submitted to the prediction challenge, indicating its broad applicability in toxicity prediction.

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Section: Calculation and Combination Of Fingerprintsmentioning

confidence: 99%

Molecular similarity-based predictions of the Tox21 screening outcome

Drwal

Siramshetty

Banerjee

et al. 2015

Front. Environ. Sci.

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“…(Yin Yang Huo), 14 Schizonopeta tenuifolia a (Jing Jie), 15 Coriandrum satiVum (Yan Sui Zi), 15 Abrus precatorius (Ji Gu Cao), 16 Impatiens balsamina (Tou Gu Cao), 16 Pinus massoniana (Song Jie), 16 Bidens pilosa (Xian Feng Cao), 17 Equisetum spp. (Mu Zei), 17 Lippia nodiflora (Peng Lai Cao), 17 Mentha spp. (Bo He), 17 AR is a key enzyme in the polyol pathway which catalyzes the NADPH-dependent reduction of glucose to sorbitol.…”

Section: C Cox and Loxmentioning

confidence: 99%

“…(Mu Zei), 17 Lippia nodiflora (Peng Lai Cao), 17 Mentha spp. (Bo He), 17 AR is a key enzyme in the polyol pathway which catalyzes the NADPH-dependent reduction of glucose to sorbitol. The latter is then converted to fructose by sorbitol dehydrogenase.…”

Section: C Cox and Loxmentioning

confidence: 99%

Virtual Screening of Chinese Herbs with Random Forest

Ehrman

Barlow

Hylands

2007

J. Chem. Inf. Model.

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Random Forest, a form of multiple decision trees, has been used to screen a database of Chinese herbal constituents for potential inhibitors against several therapeutically important molecular targets. These comprise cyclic adenosine 3′-5′-monophosphate phosphodiesterases, protein kinase A, cyclooxygenases, lipoxygenases, aldose reductase, and three HIV targets-integrase, protease, and reverse transcriptase. In addition, compounds were identified which may inhibit the expression of inducible nitric oxide synthase and/or nitric oxide production in vivo. A total of 240 Chinese herbs containing 8264 compounds were screened in silico, including many used on a regular basis in traditional Chinese medicine. Active compounds were selected from another database of 2597 phytochemicals and related natural products with known target affinities and covered a wide range of structural classes. Random Forest was found to perform well, even on highly unbalanced data characteristic of ligand-based screening where the compounds to be screened are far more numerous than the number of active compounds used in training. Despite a conservative screening protocol, a wide variety of compounds from Chinese herbs were hit. Of particular interest were the relatively large number of herbs predicted to inhibit multiple targets, as well as a number which appeared to contain inhibitors of the same target from different phytochemical classes. The latter point to the possibility that individual species may make use of alternative phytochemical strategies in target inhibition. A literature search provided evidence to support 83 herb-target predictions.

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“…These descriptors included constitutional descriptors (e.g., MW, number of rings, number of rotatable bonds), topological descriptors (e.g., Chi and Kier indices 24 ) and partial charge descriptors based on chemical graph's topology (i.e., maximum of Gasteiger/Marsali partial charges 25 ). From correlated pairs of descriptors exceeding a correlation coefficient of 0.9, only one was chosen.…”

Section: Compound Data Setsmentioning

confidence: 99%

Exploring differential evolution for inverse QSAR analysis

2017

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Inverse quantitative structure-activity relationship (QSAR) modeling encompasses the generation of compound structures from values of descriptors corresponding to high activity predicted with a given QSAR model. Structure generation proceeds from descriptor coordinates optimized for activity prediction. Herein, we concentrate on the first phase of the inverse QSAR process and introduce a new methodology for coordinate optimization, termed differential evolution (DE), that originated from computer science and engineering. Using simulation and compound activity data, we demonstrate that DE in combination with support vector regression (SVR) yields effective and robust predictions of optimized coordinates satisfying model constraints and requirements. For different compound activity classes, optimized coordinates are obtained that exclusively map to regions of high activity in feature space, represent novel positions for structure generation, and are chemically meaningful.

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The Molecular Connectivity Chi Indexes and Kappa Shape Indexes in Structure‐Property Modeling

Cited by 318 publications

References 120 publications

Molecular similarity-based predictions of the Tox21 screening outcome

Molecular similarity-based predictions of the Tox21 screening outcome

Virtual Screening of Chinese Herbs with Random Forest

Exploring differential evolution for inverse QSAR analysis

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