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Record W4327654335 · doi:10.1039/d2md00441k

Target 2035 – an update on private sector contributions

2023· editorial· en· W4327654335 on OpenAlexafffund
Suzanne Ackloo, Albert A. Antolín, José M. Bartolomé, Hartmut Beck, Alex N. Bullock, Ulrich A. K. Betz, Jark Böttcher, Peter J. Brown, Menorca Chaturvedi, Alisa Crisp, Danette L. Daniels, Jan Dreher, Kristina Edfeldt, A.M. Edwards, Ursula Egner, Jonathan M. Elkins, Christian Fischer, Tine Glendorf, Steven D. Goldberg, Ingo V. Hartung, Alexander Hillisch, Evert Homan, Stefan Knapp, Markus Köster, Oliver Krämer, Josep Llaveria, Uta Lessel, S. Lindemann, Lars Linderoth, Hisanori Matsui, Maurice Michel, Florian Montel, Anke Mueller‐Fahrnow, Susanne Müller, Dafydd R. Owen, Kumar Singh Saikatendu, Vijayaratnam Santhakumar, Wendy E. Sanderson, Cora Scholten, Matthieu Schapira, Sujata Sharma, Brock T. Shireman, M. Sundström, Matthew H. Todd, Claudia Tredup, Jennifer D. Venable, Timothy M. Willson, C.H. Arrowsmith

Bibliographic record

VenueRSC Medicinal Chemistry · 2023
Typeeditorial
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsPrincess Margaret Cancer CentreStructural Genomics ConsortiumUniversity of Toronto
FundersEshelman Institute for Innovation, University of North Carolina at Chapel HillPharmaceuticals BayerHorizon 2020 Framework ProgrammeGenentechInnovative Medicines InitiativeOntario Institute for Cancer ResearchEuropean CommissionEMD SeronoKungliga Tekniska HögskolanCanada Foundation for InnovationOntario Genomics InstituteTakeda Pharmaceutical CompanyEuropean Federation of Pharmaceutical Industries and AssociationsMerck KGaAJanssen Research and DevelopmentOntario GenomicsGenome CanadaDiamond Light SourceBoehringer IngelheimMcGill UniversityPfizer
KeywordsPrivate sectorPublic sectorPolitical scienceBusiness

Abstract

fetched live from OpenAlex

Target 2035, an international federation of biomedical scientists from the public and private sectors, is leveraging 'open' principles to develop a pharmacological tool for every human protein. These tools are important reagents for scientists studying human health and disease and will facilitate the development of new medicines. It is therefore not surprising that pharmaceutical companies are joining Target 2035, contributing both knowledge and reagents to study novel proteins. Here, we present a brief progress update on Target 2035 and highlight some of industry's contributions.

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)0.012
metaresearch head score (Gemma)0.032
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 score0.012
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.032
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0070.006
Open science0.0030.002
Research integrity0.0100.019
Insufficient payload (model declined to judge)0.0120.011

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 head0.015
GPT teacher head0.322
Teacher spread0.307 · 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

Citations19
Published2023
Admission routes2
Has abstractyes

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