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Record W3205219207 · doi:10.26434/chemrxiv.12286877.v1

Coronavirus canSAR – a Data-Driven, AI-Enabled, Drug Discovery Resource for the Research Community

2020· preprint· en· W3205219207 on OpenAlexaff
Costas Mitsopoulos, Albert A. Antolín, Eloy Villasclaras Fernandez, Patrizio Di Micco, Ioan L. Micca, Joseph E Tym, Ka Hing, James Campbell, Domenico Sanfelice, Ian Collins, Paul Workman, Bissan Al‐Lazikani

Bibliographic record

VenueChemRxiv · 2020
Typepreprint
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsInstitute of Cancer Research
FundersCancer Research UKWellcome Trust
KeywordsDrug discoveryCoronavirus disease 2019 (COVID-19)Data scienceResource (disambiguation)CoronavirusSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Computer scienceMedicineInfectious disease (medical specialty)BioinformaticsBiologyDisease

Abstract

fetched live from OpenAlex

We describe an AI-enabled, integrated Coronavirus drug discovery knowledgebase, free for the research community. Its goal is to make accessible up to date information relevant to drug discovery for SARS-CoV-2 and other coronaviruses. It builds on great knowledge from across therapeutic areas and provides unbiased, systematic, objective information to empower the international effort.

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)0.008
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science, Research integrity
Consensus categoriesOpen science
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.842
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0170.031
Research integrity0.0000.005
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 head0.370
GPT teacher head0.455
Teacher spread0.085 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2020
Admission routes1
Has abstractyes

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