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Record W4398150231 · doi:10.1039/d4dd00006d

Application of established computational techniques to identify potential SARS-CoV-2 Nsp14-MTase inhibitors in low data regimes

2024· article· en· W4398150231 on OpenAlexafffund
AkshatKumar Nigam, Matthew F. D. Hurley, Fengling Li, Eva Konkoľová, Martin Klíma, Jana Trylčová, Robert Pollice, Süleyman Selim Çınaroğlu, Roni Levin‐Konigsberg, Jasemine Handjaya, Matthieu Schapira, Irene Chau, Sumera Perveen, Ho Leung Ng, H. Ümit Kanıskan, Yulin Han, Sukrit Singh, Christoph Gorgulla, Anshul Kundaje, Jian Jin, Vincent A. Voelz, Jan Weber, Radim Nencka, Evžen Bouřa, Masoud Vedadi, Alán Aspuru‐Guzik

Bibliographic record

VenueDigital Discovery · 2024
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsVector InstituteCanadian Institute for Advanced ResearchStructural Genomics ConsortiumOntario Institute for Cancer ResearchUniversity of Toronto
FundersArmy Research LaboratoryNational Institutes of HealthSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen ForschungCanada First Research Excellence FundHelmholtz-Zentrum Berlin für Materialien und EnergieUniversity of TorontoEuropean CommissionÉcole de technologie supérieureNational Science FoundationCompute CanadaDamon Runyon Cancer Research FoundationNatural Resources CanadaNvidia
KeywordsCoronavirus disease 2019 (COVID-19)Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakSars virusComputational biologyComputer scienceEconometricsBiochemical engineeringBiologyEconomicsEngineeringVirologyMedicineInternal medicine

Abstract

fetched live from OpenAlex

Workflow used for identifying inhibitors of the SARS-CoV-2 non-structural protein 14 (nsp14), a key player in viral RNA methylation.

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)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.025
GPT teacher head0.347
Teacher spread0.322 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreMethods

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

Citations9
Published2024
Admission routes2
Has abstractyes

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