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Record W6957867354 · doi:10.6084/m9.figshare.16851421

Additional file 1 of Computational determination of toxicity risks associated with a selection of approved drugs having demonstrated activity against COVID-19

2021· article· en· W6957867354 on OpenAlexaff

Bibliographic record

VenueFigshare · 2021
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsUniversity of CalgaryUniversity of Alberta
Fundersnot available
KeywordsToxicitySelection (genetic algorithm)CarcinogenAcute toxicityRisk assessment

Abstract

fetched live from OpenAlex

Additional file 1: Supplementary Table 1. Toxicity models of acute toxicity in rats, Carcinogenicity toxicity in rat (Rat_TD50) and Carcinogenicity toxicity in mouse (Mouse_TD50(acute toxicity in rats, ra: TOX_RAT < 300), (carcinogenicity in chronic mouse studies, Xm: Mouse_TD50 < 25) and (carcinogenicity in chronic rat studies, Xr: Rat_TD50 < 4) is considered as high risk. Supplementary Table 2. Qualitative assessment of mutagenicity of the pure compound in various strains of S. typhimurium and S. typhimurium. Supplementary Table 3. Probability of metabolism by human uridine 5′-Diphosphate-Glucuronosyltransferases (UGT). We labeled Y if a given chemical structure is a substrate for UGT (1A1, 1A3, 1A6, 1A8, 1A9, 1A10 and 2B15) isozymes and (N) if it is not a substrate for UGT (1A1, 1A3, 1A6, 1A8, 1A9, 1A10 and 2B15) isozymes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.015
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.629
Threshold uncertainty score0.529

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.015
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.6290.097

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 head0.059
GPT teacher head0.315
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), not a consensus.

Study designSimulation or modeling
Domainnot available
GenreDataset

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

Citations0
Published2021
Admission routes1
Has abstractyes

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