Computational investigation of indenol derivatives as multitarget inhibitors of EGFR, VEGFR2, CDK6 and COX-2 for potential colorectal cancer therapy
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Bibliographic record
Abstract
The design of multifunctional inhibitors targeting key kinases and inflammatory enzymes represents a promising strategy in cancer therapy. In this study, a series of indenol derivatives were explored through in silico molecular docking and ADMET analysis to assess their potential as multitarget anticancer and anti-inflammatory agents. Molecular docking was performed against four relevant targets VEGFR2 (PDB ID: 3VHE ), CDK6 (PDB ID: 5L2I ), EGFR (PDB ID: 1M17 ), and COX-2 (PDB ID: 5IKR ). All compounds exhibited favorable binding energies and conserved orientations within the active sites, stabilized by hydrogen bonding, π–π stacking, and hydrophobic interactions. In VEGFR2, indenol derivatives 2, 4 , and 6 formed strong hydrogen bonds with ASP998, TYR927, and HIS1004, while in CDK6, compounds 3, 6–8 engaged the hinge region via ASP163 and PHE164, comparable to standard ATP-competitive inhibitors. Furthermore, the molecules 1–5 also showed significant affinity toward COX-2, forming hydrogen bonds with TYR385, SER530 and GLN203. ADMET profiling using ADMETlab 2.0 was deliberate in this study and revealed that all molecules 1–10 complied with Lipinski's and GSK rules, displaying molecular weights below 500 g/mol, logP values between 1.6 and 2.9, and TPSA under 140 Å 2 . Concerning the compounds 2–5 exhibited the most favorable profiles, combining high BBB permeability (logBB 0.4–0.7), moderate clearance (9–12 mL/min/kg), and low toxicity risks. These findings suggest that hydroxyl and carbonyl substituents enhance binding affinity and pharmacokinetic balance, highlighting the indenol scaffold as a valuable template for designing multitarget inhibitors with potential anticancer, anti-inflammatory, and CNS activities.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it