Nitrogen‐Containing Heterocyclic Scaffolds as EGFR Inhibitors: Design Approaches, Molecular Docking, and Structure‐Activity Relationships
Bibliographic record
Abstract
Abstract Cancer is a wide collection of diseases and among the numerous pathways involved in cancer pathogenesis, pathway involving epidermal growth factor receptor (EGFR) is one of the most prominent. EGFR frequently articulated in a variety of cancer such as breast cancer, pancreatic cancer, non‐small cell lung cancer (NSCLC), head and neck cancer. There are different EGFR tyrosine kinase inhibitors (TKIs) approved by FDA for the treatment of cancer. However, none of them evidenced as boon to oncological and medical department. Frequently occurrence of inherent and acquired resistance of TKIs as a result of mutations is the principal cause for the current situation. Therefore, researchers are in the desire of evolving the novel EGFR TKIs. Further, N ‐heterocyclic ring system always proved to be the magical weapon in designed and discovery of synthetic molecules as they acquired comprehensive range of pharmacological properties. In recent year (2018–2022) N ‐heterocyclic derivatives were uncovered as the potential EGFR TKIs. The present review summarised the research progress of EGFR TKIs to dazed the limitations of currently accessible drugs by consecrating, anatomy, mutation of EGFR, and its role in different types of cancer. The review highlights the medicinal chemistry prospective emphasising about the designing strategies, docking studies, biological evaluation, selectivity and structural activity relationship of N ‐heterocyclic compounds. Our review will support the medicinal chemists in direction for the development of novel N ‐heterocyclic based EGFR TKIs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".