Novel inhibitors of phosphate-binding enzymes as potential human therapeutics
Bibliographic record
Abstract
Studies on the discovery and development of small-molecule enzyme inhibitors are continuously growing due to their application as drugs for essentially every type of human disease, including cancer, metabolic, cardiovascular, neurodegenerative, and infectious diseases.However, the design of high affinity and membrane permeable inhibitors for phosphate-or pyrophosphatebinding enzymes with highly charged and metal-dependent active site pockets, such as the HIV-1 reverse transcriptase (HIV-1 RT) and the human geranylgeranyl pyrophosphate synthase (hGGPPS), poses a significant challenge in medicinal chemistry.In the past, the problems associated with this class of biological targets have been overcome with the development of effective phosphate or pyrophosphate bioisosteres and prodrugs or through the discovery of allosteric inhibitors.Guided by several successful incorporations of phosphate and pyrophosphate mimics, our studies were dedicated to the following: (1) identification and development of bona fide active site inhibitors of HIV-1 RT with a mechanism of action that is uniquely different from the currently known anti-HIV/AIDS drugs, and (2) discovery of potent and selective inhibitors of hGGPPS that can be used as molecular probes to investigate the role of hGGPPS in human diseases.The design, synthesis, and preliminary biological profiling of these novel compounds will be discussed.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".