Rule interpreter: a chemical language for structure-based screening

Karabunarliev, Stoyan; Nikolova, Nina; Nikolov, Nikolay; Mekenyan, Ovanes G.

doi:10.1016/s0166-1280(02)00617-6

Cited by 8 publications

(5 citation statements)

References 24 publications

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“…The study was restricted to 463 polar and non-polar narcotics taken from the recommended 694 experimental data for fish proposed by Meylan since for reactive chemicals the concept of steady state concentration in the test organism is in conflict with their reactive mode of action. The compounds were categorised as non-specifically acting toxicants and toxicants acting by specific mechanisms of action according to a substructure-based rule system for classification of chemicals [27], and the more sophisticated scheme proposed by Schultz et al [28]. The analysis was in agreement with previous models identifying a linear relationship in the range for log K ow 1 -6.…”

Section: Experimental Methods For Bcf Data Generationsupporting

confidence: 63%

Review of Literature‐Based Quantitative Structure–Activity Relationship Models for Bioconcentration

2008

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This paper reviews the state of the art of in silico methods for assessing the tendency of a substance to bioconcentrate in aquatic organisms usually expressed as its Bioconcentration Factor (BCF). It is based on an in-depth review performed by the European Chemicals Bureau of the European Commissions Joint Research Centre in support of the development of technical guidance for the implementation of the REACH legislation, and is one of a series of minireviews in this journal. The most widely used in silico approaches to estimate BCF are described, with emphasis on literature-based (Quantitative) Structure-Activity Relationship [(Q)SAR] models: (1) linear relationships that correlate the BCF with octanol -water partition coefficient; (2) (Q)SAR models based on experimentally derived descriptors and (3) new (Q)SAR models aimed to evaluate the potential of other descriptors. Recommendations for further research are also provided.

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Section: Experimental Methods For Bcf Data Generationsupporting

confidence: 63%

Review of Literature‐Based Quantitative Structure–Activity Relationship Models for Bioconcentration

2008

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“…The Molecular Query Language (MQL) provides a feature to annotate user-defined properties 17 ; however, it too requires separate enumeration of resonance structures. Attempts to incorporate physicochemical properties to various dialects of SMILES or SMARTS queries have been also reported 18,19 . Another approach to enrich the query specification with physicochemical properties is illustrated by ChemAxon's CTL 8 , where a two-step process is employed to query with features of extensible chemical terms followed by filtering based on calculated descriptor values.…”

Section: Introductionmentioning

confidence: 99%

“…The Molecular Query Language (MQL) provides a feature to annotate user-defined properties; however, it too requires separate enumeration of resonance structures. Attempts to incorporate physicochemical properties to various dialects of SMILES or SMARTS queries have been also reported. , Another approach to enrich the query specification with physicochemical properties is illustrated by ChemAxon’s CTL, where a two-step process is employed to query with features of extensible chemical terms followed by filtering based on calculated descriptor values. Although these various extensions and approaches satisfy their intended purposes, they remain proprietary or at least not publicly available, and also incompatible with each other since there have been no large and coordinated efforts to standardize query languages.…”

Section: Introductionmentioning

confidence: 99%

New Publicly Available Chemical Query Language, CSRML, To Support Chemotype Representations for Application to Data Mining and Modeling

Yang

Tarkhov

Marusczyk

et al. 2015

J. Chem. Inf. Model.

168

164

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Chemotypes are a new approach for representing molecules, chemical substructures and patterns, reaction rules, and reactions. Chemotypes are capable of integrating types of information beyond what is possible using current representation methods (e.g., SMARTS patterns) or reaction transformations (e.g., SMIRKS, reaction SMILES). Chemotypes are expressed in the XML-based Chemical Subgraphs and Reactions Markup Language (CSRML), and can be encoded not only with connectivity and topology but also with properties of atoms, bonds, electronic systems, or molecules. CSRML has been developed in parallel with a public set of chemotypes, i.e., the ToxPrint chemotypes, which are designed to provide excellent coverage of environmental, regulatory, and commercial-use chemical space, as well as to represent chemical patterns and properties especially relevant to various toxicity concerns. A software application, ChemoTyper has also been developed and made publicly available in order to enable chemotype searching and fingerprinting against a target structure set. The public ChemoTyper houses the ToxPrint chemotype CSRML dictionary, as well as reference implementation so that the query specifications may be adopted by other chemical structure knowledge systems. The full specifications of the XML-based CSRML standard used to express chemotypes are publicly available to facilitate and encourage the exchange of structural knowledge.

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“…Affinity to the AR was the subject of several SAR/QSAR studies on more or less extended sets of chemicals [2,3,5,[7][8][9][10][11][12][13]. Most of these models rely on molecular field analyses (CoMFA or similar methods).…”

Section: Introductionmentioning

confidence: 99%

Decision trees versus support vector machine for classification of androgen receptor ligands

Panaye

Doucet

Devillers³

et al. 2008

SAR and QSAR in Environmental Research

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With the current concern of limiting experimental assays, increased interest now focuses on in silico models able to predict toxicity of chemicals. Endocrine disruptors cover a large number of environmental and industrial chemicals which may affect the functions of natural hormones in humans and wildlife. Structure-activity models are now increasingly used for predicting the endocrine disruption potential of chemicals. In this study, a large set of about 200 chemicals covering a broad range of structural classes was considered in order to categorize their relative binding affinity (RBA) to the androgen receptor. Classification of chemicals into four activity groups, with respect to their log RBA value, was carried out in a cascade of recursive partitioning trees, with descriptors calculated from CODESSA software and encoding topological, geometrical and quantum chemical properties. The hydrophobicity parameter (log P), Balaban index, and descriptors relying on charge distribution (maximum partial charge, nucleophilic index on oxygen atoms, charged surface area, etc.) appear to play a major role in the chemical partitioning. Separation of strongly active compounds was rather straightforward. Similarly, about 90% of the inactive compounds were identified. More intricate was the separation of active compounds into subsets of moderate and weak binders, the task requiring a more complex tree. A comparison was made with support vector machine yielding similar results.

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Rule interpreter: a chemical language for structure-based screening

Cited by 8 publications

References 24 publications

Review of Literature‐Based Quantitative Structure–Activity Relationship Models for Bioconcentration

Review of Literature‐Based Quantitative Structure–Activity Relationship Models for Bioconcentration

New Publicly Available Chemical Query Language, CSRML, To Support Chemotype Representations for Application to Data Mining and Modeling

Decision trees versus support vector machine for classification of androgen receptor ligands

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