DRESIS 2.0: the comprehensive landscape of drug resistance information
Bibliographic record
Abstract
Elucidating mechanisms of drug resistance is key for overcoming resistance, guiding drug design, and enabling accurate resistance prediction. Recently, disease metabolic reprogramming has emerged as a novel mechanism of resistance, which enables disease cells to adapt to therapeutic resistance by altering energy production pathways, cellular signaling, and biosynthesis processes. Moreover, protein structure alterations also play a pivotal role in resistance study, facilitating mechanistic understanding, and structure-based target discovery. In other words, integrating these recently accumulated critical data is essential for enriching the landscape of drug resistance data. Therefore, in this study, DRESIS was a significant update by providing (i) 236 molecules that drive metabolic reprogramming and confer resistance to 168 drugs, together with a detailed mechanism, (ii) 2228 protein structural variants implicated in resistance to 671 drugs across 238 diseases, and (iii) greatly expanded landscapes of drug resistance information, now featuring 398 newly added key drug-resistant molecules, 356 drugs with the latest published resistance mechanisms, and 81 new drug-resistant disease categories. All in all, DRESIS 2.0 is expected to serve as a valuable resource for the scientific community and provide important support in tackling the global challenge of drug resistance, which is now publicly accessible at https://idrblab.org/dresis/.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.021 | 0.025 |
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".