In silico development of novel angiotensin-converting-enzyme-I inhibitors by Monte Carlo optimization based QSAR modeling, molecular docking studies and ADMET predictions.

Within the realm of pharmacological strategies for cardiovascular diseases (CVD) like hypertension, stroke, and heart failure, targeting the angiotensin-converting enzyme I (ACE-I) stands out as a significant treatment approach. This study employs QSAR modeling using Monte Carlo optimization techniques to investigate a range of compounds known for their ACE-I inhibiting properties. The modeling process involved leveraging local molecular graph invariants and SMILES notation as descriptors to develop conformation-independent QSAR models. The dataset was segmented into distinct sets for training, calibration, and testing to ensure model accuracy. Through the application of various statistical analyses, the efficacy, reliability, and predictive capability of the models were evaluated, showcasing promising outcomes. Additionally, molecular fragments derived from SMILES notation descriptors were identified to elucidate the activity changes observed in the compounds. The validation of the QSAR model and designed inhibitors was carried out via molecular docking, aligning well with the QSAR results. To ascertain the drug-worthiness of the designed molecules, their physicochemical properties were computed, aiding in the prediction of ADME parameters, pharmacokinetic attributes, drug-likeness, and medicinal chemistry compatibility. [Display omitted] • QSAR models for ACE-I inhibition were developed. • Monte Carlo optimization method was employed to construct QSAR models. • SMILES notation descriptors and local molecular graph invariants were employed. • Different methods were applied for QSPR models predictability determination. • Molecular fragments with influence on studied activity were determined. [ABSTRACT FROM AUTHOR]