Using DrugBank for <i>In Silico</i> Drug Exploration and Discovery
Bibliographic record
Abstract
DrugBank is a fully curated drug and drug target database that contains 8174 drug entries including 1944 FDA approved small-molecule drugs, 198 FDA-approved biotech (protein/peptide) drugs, 93 nutraceuticals, and over 6000 experimental drugs. Additionally, 4300 non-redundant protein (i.e., drug target/enzyme/transporter/carrier) sequences are linked to these drug entries. DrugBank is primarily focused on providing both the query/search tools and 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 tumor samples. The intent of this unit is to give readers an introduction to the field of Web-based drug discovery and to show how cheminformatics can be seamlessly integrated into the field of bioinformatics. © 2016 by John Wiley & Sons, Inc.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.049 | 0.049 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".