Exploring the Potential of Pyridine Carboxylic Acid Isomers to Discover New Enzyme Inhibitors
Bibliographic record
Abstract
Pyridine carboxylic acid isomers - picolinic acid, nicotinic acid, and isonicotinic acid - have historically resulted in a plethora of drugs against tuberculosis, cancer, diabetes, Alzheimer's, angina, dementia, depression, allergy, respiratory acidosis, psoriasis, acne, hypertension, hyperlipidemia, HIV/AIDS (specifically HIV-1), among others. Despite the large number of therapeutic agents derived from these isomers, the research involving these scaffolds is still exceptionally active. The current surge in enzyme inhibitory activities by the compounds derived from them has further created space for the discovery of new drug candidates. This review focuses on the medicinal relevance of these isomers by analyzing structure-activity relationships (SARs) and highlighting emerging trends from patents filed over the last decade. Notably, pharmaceutical giants like Bayer, Bristol-Myers Squibb, Novartis, Curis, and Aurigene have developed enzyme inhibitors based on these scaffolds with nanomolar potency. The role of these isomers in the development of antiviral agents, including protease inhibitors, is also discussed. Overall, this review brings to the readers, a pragmatic opportunity to comprehend the recent literature, highlighting the scaffolds' importance in the design of new enzyme inhibitors. Furthermore, it discusses the structure-activity relationship of pyridine carboxylic acid-derived compounds and highlights the current patenting trends in medicinal chemistry.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".