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Record W2209605205 · doi:10.1021/ci060142y

Representation of Chemical Information in OASIS Centralized 3D Database for Existing Chemicals

2006· article· en· W2209605205 on OpenAlexaboutno aff
Nikolai Georgiev Nikolov, Vanio Grancharov, Galya Stoyanova, Todor Pavlov, Ovanes Mekenyan

Bibliographic record

VenueJournal of Chemical Information and Modeling · 2006
Typearticle
Languageen
FieldChemistry
TopicHistory and advancements in chemistry
Canadian institutionsnot available
Fundersnot available
KeywordsDatabaseChemical databaseChemical industryChristian ministryConformational isomerismFlexibility (engineering)Computer scienceChemical nomenclatureOrganic chemicalsQuantum chemicalChemistryInformation retrievalMoleculeEnvironmental chemistryMathematicsOrganic chemistry

Abstract

fetched live from OpenAlex

The present inventory of existing chemicals in regulatory agencies in North America and Europe, encompassing the chemicals of the European Chemicals Bureau (EINECS, with 61 573 discrete chemicals); the Danish EPA (159 448 chemicals); the U.S. EPA (TSCA, 56 882 chemicals; HPVC, 10 546 chemicals) and pesticides' active and inactive ingredients of the U.S. EPA (1379 chemicals); the Organization for Economic Cooperation and Development (HPVC, 4750 chemicals); Environment Canada (DSL, 10851 chemicals); and the Japanese Ministry of Economy, Trade, and Industry (16811), was combined in a centralized 3D database for existing chemicals. The total number of unique chemicals from all of these databases exceeded 185 500. Defined and undefined chemical mixtures and polymers are handled, along with discrete (hydrolyzing and nonhydrolyzing) chemicals. The database manager provides the storage and retrieval of chemical structures with 2D and 3D data, accounting for molecular flexibility by using representative sets of conformers for each chemical. The electronic and geometric structures of all conformers are quantum-chemically optimized and evaluated. Hence, the database contains over 3.7 million 3D records with hundreds of millions of descriptor data items at the levels of structures, conformers, or atoms. The platform contains a highly developed search subsystem--a search is possible on Chemical Abstracts Service numbers; names; 2D and 3D fragment searches; structural, conformational, or atomic properties; affiliation in other chemical databases; structure similarity; logical combinations; saved queries; and search result exports. Models (collections of logically related descriptors) are supported, including information on a model's author, date, bioassay, organs/tissues, conditions, administration, and so forth. Fragments can be interactively constructed using a visual structure editor. A configurable database browser is designed for the inspection and editing of all types of data items. Database statistics are maintained on the number and quality of structures, conformers, and descriptors. Reports can be generated presenting any chosen subset of structures and descriptors into different formats suitable for inclusion into documents. In addition to fixed report formats, there is a powerful report template designer module with a visual report template editor to produce a customized page layout. The database is compatible at the import/export level with SDF, MOL, SMILES, and other known formats. The precalculated centralized 3D database could be useful for quantitative structure-activity relationship developers avoiding the time-consuming and cumbersome 3D calculation phase of model development.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.121
Threshold uncertainty score0.533

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.044
GPT teacher head0.320
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations16
Published2006
Admission routes1
Has abstractyes

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