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Record W2054916058 · doi:10.2174/1568026023394470

Multivariate Pharmaceutical Profiling for Drug Discovery

2002· review· en· W2054916058 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueCurrent Topics in Medicinal Chemistry · 2002
Typereview
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsWomen's Health Research Institute
Fundersnot available
KeywordsProfiling (computer programming)Drug discoveryDrugComputer scienceComputational biologyData scienceMedicinePharmacologyBioinformaticsBiology

Abstract

fetched live from OpenAlex

The field of pharmaceutical profiling in drug discovery is described. The pharmaceutical properties of drug candidates determine how much of the drug safely reaches the therapeutic target. Drug candidates often fail in discovery and development due to inadequate properties, resulting in lost opportunities and resources for developing new drugs. Pharmaceutical profiling assays have been developed and implemented to measure the properties of large numbers of drug candidates starting at the earliest stages of discovery. This information is used for informed decisions in drug candidate selection and synthetic optimization. A holistic process of parallel activity and property optimization has emerged in drug discovery. The assays, strategies, and data management associated with pharmaceutical profiling are discussed.

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.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.971
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.162
GPT teacher head0.465
Teacher spread0.303 · 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