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Record W1185328029 · doi:10.15200/winn.143518.87488

The Functional SMILES Perspective

2015· dataset· en· W1185328029 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Winnower · 2015
Typedataset
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsNotationPerspective (graphical)Chemical nomenclatureSmiles rearrangementInterpretation (philosophy)Reading (process)Computer scienceENCODEChemistryArtificial intelligenceMathematicsStereochemistryLinguisticsProgramming languageArithmeticPhilosophy

Abstract

fetched live from OpenAlex

Simplified Molecular-Input Line-Entry System or SMILES is a notation scheme for representing chemical structures in a single line of text, encoding atom connectivity and stereochemistry, as well as charge and ring structures. There are a large number of possible SMILES notations for any one chemical structure, which has led to the development of the canonical SMILES notation. In contrast, I describe here a SMILES approach or “perspective” which encodes functional groups into valid SMILES strings. It is shown that this functional SMILES perspective further simplifies the human interpretation of SMILES strings, can be easily formed from reading IUPAC nomenclature, and has the ability to encode limited chemical reaction histories.

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.

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)0.002
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: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.051
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0030.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.040
GPT teacher head0.321
Teacher spread0.281 · 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