Development of Novel ROCK Inhibitors via 3D-QSAR and Molecular Docking Studies: A Framework for Multi-Target Drug Design
Bibliographic record
Abstract
Background/Objectives: Alterations in the actin cytoskeleton correlates to tumor progression and affect critical cellular processes such as adhesion, migration and invasion. Rho-associated coiled-coil-containing protein kinases (ROCK1 and ROCK2), important regulators of the actin cytoskeleton, are frequently overexpressed in various malignancies. The aim of this study was therefore to identify the key structural features of ROCK1/ROCK2 inhibitors using computer-aided drug design (CADD) approaches. In addition, new developed ROCK inhibitors provided a significant framework for the development of multitarget therapeutics—ROCK/HDAC (histone deacetylases) multitarget inhibitors. Methods: 3D-QSAR (Quantitative structure-activity relationship study) and molecular docking study were employed in order to identify key structural features that positively correlate with ROCK inhibition. MDA-MB-231, HCC1937, Panc-1 and Mia PaCa-2 cells were used for evaluation of anticancer properties of synthesized compounds. Results: C-19 showed potent anti-cancer properties, especially enhancement of apoptosis and cell cycle modulation in pancreatic cancer cell lines. In addition, C-19 and C-22 showed potent anti-migratory and anti-invasive effects comparable to the well-known ROCK inhibitor fasudil. Conclusions: In light of the results of this study, we propose a novel multi-target approach focusing on developing dual HDAC/ROCK inhibitors based on the structure of both C-19 and C-22, exploiting the synergistic potential of these two signaling pathways to improve therapeutic efficacy in metastatic tumors. Our results emphasize the potential of multi-target ROCK inhibitors as a basis for future cancer therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".