High Throughput Crystallography of TB Drug Targets
Bibliographic record
Abstract
Tuberculosis (TB) infects one-third of the world population. Despite 50 years of available drug treatments, TB continues to increase at a significant rate. The failure to control TB stems in part from the expense of delivering treatment to infected individuals and from complex treatment regimens. Incomplete treatment has fueled the emergence of multi-drug resistant (MDR) strains of Mycobacterium tuberculosis (Mtb). Reducing non-compliance by reducing the duration of chemotherapy will have a great impact on TB control. The development of new drugs that either kill persisting organisms, inhibit bacilli from entering the persistent phase, or convert the persistent bacilli into actively growing cells susceptible to our current drugs will have a positive effect. We are taking a multidisciplinary approach that will identify and characterize new drug targets that are essential for persistent Mtb. Targets are exposed to a battery of analyses including microarray experiments, bioinformatics, and genetic techniques to prioritize potential drug targets from Mtb for structural analysis. Our core structural genomics pipeline works with the individual laboratories to produce diffraction quality crystals of targeted proteins, and structural analysis will be completed by the individual laboratories. We also have capabilities for functional analysis and the virtual ligand screening to identify novel inhibitors for target validation. Our overarching goals are to increase the knowledge of Mtb pathogenesis using the TB research community to drive structural genomics, particularly related to persistence, develop a central repository for TB research reagents, and discover chemical inhibitors of drug targets for future development of lead compounds.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
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".