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Record W2806538628 · doi:10.1111/cts.12570

<i>Assay Guidance Manual</i>: Quantitative Biology and Pharmacology in Preclinical Drug Discovery

2018· review· en· W2806538628 on OpenAlexaff
Nathan P. Coussens, G. Sitta Sittampalam, Rajarshi Guha, Kyle R. Brimacombe, A Grossman, Thomas D.Y. Chung, Jeffrey R. Weidner, Terry Riss, O. Joseph Trask, Douglas S. Auld, Jayme L. Dahlin, Viswanath Devanaryan, Timothy L. Foley, J McGee, Steven D. Kahl, Stephen C. Kales, Michelle R. Arkin, Jonathan B. Baell, B Bejcek, Neely Gal‐Edd, Marcie A. Glicksman, Joseph V. Haas, Philip W. Iversen, Marilu Hoeppner, Stacy Lathrop, Eric W Sayers, Hanguan Liu, Barton W Trawick, Julie McVey, Vance Lemmon, Zhuyin Li, Owen B. McManus, Lisa Minor, Andrew D. Napper, Mary Jo Wildey, Robert E. Pacifici, William W. Chin, Menghang Xia, Xin Xu, Madhu Lal‐Nag, Matthew D. Hall, Sam Michael, James Inglese, Anton Simeonov, Christopher P. Austin

Bibliographic record

VenueClinical and Translational Science · 2018
Typereview
Languageen
FieldImmunology and Microbiology
TopicBiosimilars and Bioanalytical Methods
Canadian institutionsDiscovery Centre
FundersU.S. National Library of MedicineNational Institute of General Medical SciencesNational Center for Advancing Translational SciencesNational Institute of Mental HealthNational Institutes of Health
KeywordsDrug discoveryDrug developmentComputational biologyInvestigational DrugsPreclinical testingPreclinical researchPharmacologyComputer scienceMedicineDrugMedical physicsBioinformaticsBiologyClinical trial

Abstract

fetched live from OpenAlex

The Assay Guidance Manual (AGM) is an eBook of best practices for the design, development, and implementation of robust assays for early drug discovery. Initiated by pharmaceutical company scientists, the manual provides guidance for designing a "testing funnel" of assays to identify genuine hits using high-throughput screening (HTS) and advancing them through preclinical development. Combined with a workshop/tutorial component, the overall goal of the AGM is to provide a valuable resource for training translational scientists.

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)0.005
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.007
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0050.005
Science and technology studies0.0010.002
Scholarly communication0.0030.004
Open science0.0030.002
Research integrity0.0030.007
Insufficient payload (model declined to judge)0.0270.047

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 head0.170
GPT teacher head0.526
Teacher spread0.357 · 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 designNot applicable
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

Citations51
Published2018
Admission routes1
Has abstractyes

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