Exploring the Structural Diversity of Novel 3-Substituted Coumarins as Potent Inhibitors of Tumor-Associated Carbonic Anhydrases: Expanding the Pharmacological Space for Anticancer Drug Discovery
Bibliographic record
Abstract
Abstract Background Innovations in cancer chemotherapy continue to occupy the priority list of demands to ensure our health security. The vast chemical space provides a plethora of anticancer discovery opportunities, however, limited by the boundaries of synthetic feasibility. Objective Expand the established pharmacological space of tumor-associated carbonic anhydrases by exploring the synthetically feasible chemical space of 3-substituted coumarins. Method A series of 52 novel 3-substituted coumarins were randomly sketched by our team of synthetic chemists with priority given to synthetic feasibility. The pharmacological potentials of the novel coumarin series were computationally estimated using a machine-learning approach exploiting both chemical and statistical inference. 17 members of the novel series were predicted to possess cytotoxic activity against HeLa cells by interfering with the tumor-associated carbonic anhydrases IX and XII. Those 17 compounds were synthesized and biologically tested against HeLa cells, subsequently; the 3 most potent compounds were assayed against carbonic anhydrases I, II, IX, and XII employing Acetazolamide as a reference. The molecular binding mechanism of those 3 chosen compounds with the four enzyme isoforms was studied using molecular docking simulation. Result Most of the compounds exhibited competent inhibitory activity against HeLa cells. The carbonic anhydrase inhibition results unveiled the powerful but non-selective nature of those suicide inhibitors. Conclusion Novel 3-substituted coumarins have been dispatched to join the pharmacological space of tumor-associated carbonic anhydrases’ suicide inhibitors.
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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 | 0.000 | 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) | 0.001 | 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 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".