Bibliographic record
Abstract
Natural products derived from plants, marine organisms and microorganisms exhibit interesting anti-microbial, anti-viral, and anti-inflammatory activities. These bioactivities make natural products an important source for the discovery of new pharmaceutical agents, and more than 60% of the drugs available on the market are of natural product origin. One of the aspects of drug discovery process is the identification of small molecules with enzyme-inhibiting activities. Enzymes are essential to human life, mediating biochemical processes including metabolism, cellular signal transduction, cell cycling, and development. Malfunction in these biochemical systems often leads to disease that can be caused either by the dysfunction, overexpression, or hyper-activation of the enzymes involved. An understanding of diseases at the molecular level has provided several enzyme inhibitors in clinics. For instance, galanthamine, a potent acetylcholinesterase (AChE) inhibitor, is used to treat Alzheimer's disease. In-vitro enzyme inhibition assays can easily be performed in any natural product chemistry lab in order to discover lead compounds against various biological targets. For instance, glutathione S-transferase (GST) inhibitors have applications in overcoming the drug resistance problems in cancer and parasite chemotherapy. Fatty acid synthase inhibitors are used to discover anti-malarial, anti-parasitic, anti-TB and anti-fungal compounds. We are involved in discovering new enzyme inhibitors from medicinally important plants. These efforts have resulted in the identification of a few novel AChE, GST and α-glucosidase inhibitors. During this presentation, structure elucidation of these new enzyme inhibitors and their bioactivity data will be discussed. Additionally, results obtained from structure-activity relationship studies will also be presented.
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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) | 0.000 | 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 | 0.001 |
| 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".