Abstract
Background: Depression, a neurological disorder, is globally the 4th leading cause of chronic disabilities in human beings.
Objective: This study aimed to model a 2D-QSAR equation that can facilitate the researchers to design better aplysinopsin analogs with potent hMAO-A inhibition.
Methods: Aplysinopsin analogs dataset were subjected to ADME assessment for drug-likeness suitability using StarDrop software before modeled equation. 2D-QSAR equations were generated using VLife MDS 4.6. Dataset was segregated into training and test set using different methodologies, followed by variable selection. Model development was done using principal component regression, partial least square regression, and multiple regression.
Results: The dataset has successfully qualified the drug-likeness criteria in ADME simulation, with more than 90% of molecules cleared the ideal conditions, including intrinsic solubility, hydrophobicity, CYP3A4 2C9pKi, hERG pIC50, etc. 112 models were developed using multiparametric consideration of methodologies. The best six models were discussed with their extent of significance and prediction capabilities. ALP97 was emerged out as the most significant model out of all, with similar to 83% of the variance in the training set, the internal predictive ability of similar to 74%, while having the external predictive capability of similar to 79%.
Conclusion: ADME assessment suggested that aplysinopsin analogs are worth investigating. Interaction among the descriptors in the way of summation or multiplication products are quite influential and yield significant 2D-QSAR models with good prediction efficiency. This model can be used to design a more potent hMAO-A inhibitor with an aplysinopsin scaffold, which can then contribute to the treatment of depression and other neurological disorders.