Background: Alzheimer’s disease (AD), the most common cause of dementia in the elderly, is a progressive neurodegenerative disorder that gradually affects cognitive function and eventually causes death. Most approved drugs can only treat the disease alleviating the disease symptoms; therefore, there is a need to develop drugs that can treat this illness holistically. The medical community is searching for new drugs and new drug targets to cure this disease. In this study, QSAR, molecular docking evaluation, and ADMET/pharmacokinetics assessment were used as modeling methods to identify the compounds with outstanding physicochemical properties. Results: The 37 MAO-B compounds were screened using the aforementioned methods and yielded a model with the following molecular properties: AATS1v, AATS3v, GATS4m, and GATS6e. Good statistical values were R²_train = 0.69, R²_adj = 0.63, R²_pred = 0.57, LOF = 0.23, and RMSE = 0.38. The model was validated using an evaluation set that confirmed its robustness. The molecular docking was also utilized using crystal structure of human monoamine oxidase B in complex with chlorophenylchromone-carboxamide with ID code of 6FW0, and three compounds were identified with outstanding high binding affinity (13 = − 30.51 kcal mol⁻¹, 31 = − 31.85 kcal mol⁻¹, and 33 = − 33.70 kcal mol⁻¹), and better than the Eldepryl (referenced) drug (− 11.40 kcal mol⁻¹). Conclusions: These three compounds (13, 31, and 33) were analyzed for ADMET/pharmacokinetics evaluation and found worthy of further analysis as promising drug candidates to cure AD and could also serve as a template to design several monoamine oxidase B inhibitors in the future to cure AD.

Quantitative structure-activity relationship (QSAR) models were combined with density functional computations and used to predict anti-fungi activities in a series of coumarin derivatives. Essential descriptors employed in this study were chosen based on the use of the Genetic Function Approximation (GFA) method. Leave-N-Out (LNO) and Y-randomization techniques affirmed the model’s robustness and validity. Computed pMIC values were found to be in good agreement (+/- XX%) with experimentally determined values. The proposed model may be a superior predictor of the counter-parasitic action of coumarin analogs and can be utilized for recommendation of new chemopreventive species.