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Record W2996359815 · doi:10.1101/2019.12.22.886523

The Kinase Chemogenomic Set (KCGS): An open science resource for kinase vulnerability identification

2019· preprint· en· W2996359815 on OpenAlexfundno aff
Carrow I. Wells, Hassan Al‐Ali, David Andrews, Christopher R. M. Asquith, Alison D. Axtman, Mirra Chung, Ivan Đikić, Daniel Ebner, Jonathan M. Elkins, Peter Ettmayer, Christian Fischer, Mathias Frederiksen, Nathanael S. Gray, Stephanie B. Hatch, Stefan Knapp, Shudong Lee, Ulrich Lücking, Michel Michaelides, Caitlin E. Mills, Susanne Müller, Dafydd R. Owen, Alfredo Picado, Kristijan Ramadan, Kumar Singh Saikatendu, Martin Schröder, Alexandra Stolz, Mariana Tellechea, Daniel K. Treiber, Brandon J. Turunen, Santiago Vilar, Jinhua Wang, William J. Zuercher, Timothy M. Willson, David H. Drewry

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsnot available
FundersMedical Research CouncilNational Institutes of HealthMinistero dello Sviluppo EconomicoFundação de Amparo à Pesquisa do Estado de São PauloLineberger Comprehensive Cancer Center, University of North Carolina at Chapel HillNovartis PharmaWellcome TrustOntario Ministry of Economic Development and InnovationGenome CanadaPfizer
KeywordsKinaseIdentification (biology)Set (abstract data type)Computational biologyComputer scienceVulnerability (computing)BiologyCell biology

Abstract

fetched live from OpenAlex

Abstract We describe the assembly and annotation of a chemogenomic set of protein kinase inhibitors as an open science resource for studying kinase biology. The set only includes inhibitors that show potent kinase inhibition and a narrow spectrum of activity when screened across a large panel of kinase biochemical assays. Currently, the set contains 187 inhibitors that cover 215 human kinases. The kinase chemogenomic set (KCGS) is the most highly annotated set of selective kinase inhibitors available to researchers for use in cell-based screens.

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)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesOpen science
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 score0.998
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.007
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0130.010

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 head0.039
GPT teacher head0.314
Teacher spread0.275 · 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 designNot applicable
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

Citations12
Published2019
Admission routes1
Has abstractyes

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