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Record W2137827821 · doi:10.1002/0471250953.bi1404s18

In Silico Drug Exploration and Discovery Using DrugBank

2007· article· en· W2137827821 on OpenAlexafffund
David S. Wishart

Bibliographic record

VenueCurrent Protocols in Bioinformatics · 2007
Typearticle
Languageen
FieldComputer Science
TopicComputational Drug Discovery Methods
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersGenome Alberta
KeywordsDrugBankIn silicoDrug discoveryComputational biologyDrugComputer sciencePharmacologyMedicineBioinformaticsBiology

Abstract

fetched live from OpenAlex

DrugBank is a fully curated drug and drug target database that contains information on nearly 5000 drugs, including > 1200 FDA-approved small molecule and biotech drugs as well as >3200 experimental drugs. Additionally, more than 14,000 protein or drug target sequences are linked to these drug entries. DrugBank is primarily focused on providing both the query/search tools and the biophysical data needed to facilitate drug discovery and drug development. This unit provides readers with a detailed description of how to effectively use the DrugBank database and how to navigate through the DrugBank Web site. It also provides specific examples of how to find chemical homologs of potential drug leads and how to identify potential drug targets from newly sequenced pathogens. The intent of this unit is to give readers some introduction into the field of cheminformatics (the study of chemical information) and to show how cheminformatics can be seamlessly integrated into the field of bioinformatics.

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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.018
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.000
Scholarly communication0.0020.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.004

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 head0.079
GPT teacher head0.406
Teacher spread0.327 · 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 designSimulation or modeling
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

Citations12
Published2007
Admission routes2
Has abstractyes

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