QSAR-Guided Optimisation of Benzimidazole Derivatives as Putative CDK4 Inhibitors in Pancreatic Ductal Adenocarcinoma
Keywords:
benzimidazole, CDK4, pancreatic cancer, QSAR, docking, ADMETAbstract
Background: Pancreatic ductal adenocarcinoma has limited therapeutic options and commonly exhibits dysregulated cyclin-dependent kinase activity. Benzimidazole chemistry offers a flexible scaffold for kinase-directed optimisation.
Objective: To derive a predictive quantitative structure–activity relationship (QSAR) model and identify benzimidazole derivatives with improved CDK4 binding and acceptable developability profiles.
Methods: A training set of 112 structurally related compounds with reported antiproliferative activity was standardised and divided into training and external-test cohorts. Molecular descriptors were reduced using variance and collinearity thresholds before elastic-net and gradient-boosted regression. Applicability-domain analysis was performed. Twenty virtual analogues generated from the best scaffold were docked to CDK4 and assessed for ADMET liabilities.
Results: The final gradient-boosted model achieved R²=0.79 and RMSE=0.44 on the external test set. Aromatic heteroatom distribution, topological polar surface area and ring-associated electronic descriptors were the strongest predictors. Four virtual analogues fell within the applicability domain and exceeded the parent scaffold’s predicted activity. BZ-17 produced the most favourable CDK4 docking score (-9.6 kcal/mol), forming persistent interactions with Val96 and the hinge region. Predicted solubility improved by 0.7 log units relative to the parent compound, while lipophilicity remained within the prespecified range. Two analogues were excluded because of predicted CYP inhibition and mutagenicity alerts.
Conclusion: QSAR-guided analogue generation narrowed a broad chemical space to a small set of experimentally actionable benzimidazole candidates. BZ-17 combined strong model-predicted potency, plausible CDK4 engagement and a comparatively cleaner in silico safety profile, supporting synthesis and biochemical validation in pancreatic-cancer models.
Publication format: research abstract. No full-text manuscript accompanies this record.
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