Identification of Drug Target Properties and its validation on Helicobacter pylori
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.
Bibliographic record
Abstract
An analysis of 472 proteins taken from the drug target database and considered as bacterial drug targets has been carried out. A number of sequential properties viz. length, molecular weight, hydrophobicity, cellular localization, transmembrane helices, glycosylation and signal peptide were determined. Based on these properties, a range was set for each property and it was considered that a protein can be a drug target if that protein property comes in the same range. To validate, the method was applied to Helicobacter pylori having 1602 proteins. The properties were calculated for proteins from H. pylori and the range was applied to find the drug target. After analysis of the whole proteome, 5 proteins have been found to have all the properties in the range. The results were cross checked and it has been found that the resultant proteins are also drug targets for other pathogens. It indicated that the sequential properties of successful target help in finding the new drug target for the pathogen.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it