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Record W4205976291 · doi:10.3390/molecules25132968

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes–7

2020· editorial· en· W4205976291 on OpenAlexaff
Michael Gütschow, Jean Jacques Vanden Eynde, Josef Jampílek, CongBao Kang, Arduino A. Mangoni, Paola Fossa, Rafik Karaman, Andrea Trabocchi, Peter J. H. Scott, Jóhannes Reynisson, Simona Rapposelli, Stefania Galdiero, Jean‐Yves Winum, Chiara Brullo, Katalin Prókai-Tátrai, Arun Sharma, Matthieu Schapira, Yasu‐Taka Azuma, Laura Cerchia, Mariana Spetea, Giangiacomo Torri, Simona Collina, Athina Geronikaki, Alfonso T. García‐Sosa, M. Helena Vasconcelos, Emı́lia Sousa, Ivan Kosalec, Tiziano Tuccinardi, Iola F. Duarte, Jorge A. R. Salvador, Massimo Bertinaria, Maurizio Pellecchia, Jussara Amato, Giulio Rastelli, Paula Gomes, Rita C. Guedes, Jean‐Marc Sabatier, Ana Estévez‐Braun, Bruno Pagano, Stefano Mangani, Rino Ragno, George Kokotos, Margherita Brindisi, Florenci V. González, Fernanda Borges, Mariarosaria Miloso, Jarkko Rautio, Diego Muñoz‐Torrero

Bibliographic record

VenueMolecules · 2020
Typeeditorial
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsStructural Genomics ConsortiumCanada Research ChairsUniversity of Toronto
FundersNational Institute of Neurological Disorders and StrokeNational Cancer Institute
KeywordsChemistryNanotechnologyEngineering ethicsEngineeringMaterials science

Abstract

fetched live from OpenAlex

Breakthroughs in Medicinal Chemistry: New Targets and Mechanisms, New Drugs, New Hopes is a series of editorials which is published on a biannual basis by the Editorial Board of the Medicinal Chemistry section of the journal Molecules [...]

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)0.008
metaresearch head score (Gemma)0.021
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: Editorial · Consensus signal: Editorial
Teacher disagreement score0.037
Threshold uncertainty score0.123

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.021
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.001
Science and technology studies0.0020.003
Scholarly communication0.0090.005
Open science0.0020.002
Research integrity0.0060.012
Insufficient payload (model declined to judge)0.0370.027

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 head0.009
GPT teacher head0.267
Teacher spread0.259 · 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
GenreEditorial

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

Citations6
Published2020
Admission routes1
Has abstractyes

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