PRACTICAL APPLICATIONS OF MACHINE LEARNING FOR ANTI-INFECTIVE DRUG DISCOVERY
Bibliographic record
Abstract
ADVERTISEMENT RETURN TO BOOKPREVChapterNEXTPRACTICAL APPLICATIONS OF MACHINE LEARNING FOR ANTI-INFECTIVE DRUG DISCOVERYNishant SarkarNishant SarkarDepartment of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, and David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, CanadaMore by Nishant Sarkar and Jonathan M. StokesJonathan M. StokesDepartment of Biochemistry and Biomedical Sciences, Michael G. DeGroote Institute for Infectious Disease Research, and David Braley Centre for Antibiotic Discovery, McMaster University, Hamilton, Ontario, CanadaMore by Jonathan M. StokesDOI: 10.1021/mc-2023-vol58.ch14Publication Date (Web):November 16, 2023Publication History Published online16 November 2023Request reuse permissions Copyright © 2023 MEDI, Inc. Published by American Chemical Society.2023 Medicinal Chemistry ReviewsChapter 14pp 345-375Medicinal Chemistry ReviewsVol. 58ISBN13: 9781734427462eISBN: 9781734427462Article Views72Altmetric-Citations-LEARN ABOUT THESE METRICSArticle Views are the COUNTER-compliant sum of full text article downloads since November 2008 (both PDF and HTML) across all institutions and individuals. These metrics are regularly updated to reflect usage leading up to the last few days.Citations are the number of other articles citing this article, calculated by Crossref and updated daily. Find more information about Crossref citation counts.The Altmetric Attention Score is a quantitative measure of the attention that a research article has received online. Clicking on the donut icon will load a page at altmetric.com with additional details about the score and the social media presence for the given article. Find more information on the Altmetric Attention Score and how the score is calculated. Share Add toView InAdd Full Text with ReferenceAdd Description ExportRISCitationCitation and abstractCitation and referencesMore Options Share onFacebookTwitterWechatLinked InReddit Read OnlinePDF (2 MB) Get e-Alerts
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.031 | 0.014 |
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".