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|Title:||Multi-objective optimization of benzamide derivatives as RHO kinase inhibitors|
|Citation:||GAJO, G. C. et al. Multi-objective optimization of benzamide derivatives as RHO kinase inhibitors. Molecular Informatic, [S.l.], v. 37, n. 3, Mar. 2018.|
|Abstract:||Despite recent advances in Computer Aided Drug Discovery and High Throughput Screening, the attrition rates of drug candidates continue to be high, underscoring the inherent complexity of the drug discovery paradigm. Indeed, a compromise between several objectives is often required to obtain successful clinical drugs. The present manuscript details a multi‐objective workflow that integrates the 4D‐QSAR and molecular docking methods in the simultaneous modeling of the Rho Kinase inhibitory activity and acute toxicity of Benzamide derivatives. To this end, the pIC50/pLD50 ratio is considered as the response variable, permitting the concurrent modeling of both properties and representing a shift from classical step‐by‐step evaluations. The 4D‐QSAR strategy is used to generate the Grid Cell Occupancy Descriptors (GCODs), and Stochastic Gradient Boosting (SGB) and Partial Least Squares (PLS) methods as the model fitting techniques. While the statistical parameters for the PLS model do not meet established criteria for acceptability, the SGB model yields satisfactory performance, with correlation coefficients r2=0.95 and r2pred=0.65 for the training and test set, respectively. Posteriorly, the structural interpretation of the most relevant GCODs according to the SGB model is performed, allowing for the proposal of 139 novel benzamide derivatives, which are then screened using the same model. Of these 9 compounds were predicted to possess pIC50/pLD50 ratio values higher than those for the employed dataset. Finally, in order to corroborate the results obtained with the SGB model, a docking simulation was formed to evaluate the binding affinity of the proposed molecules to the ROCK2 active site and 3 chemical structures (i. e. p6, p14 and p131) showed higher binding affinity than the most active compound in the training set, while the rest generally demonstrated comparable behavior. It may therefore be concluded that the consensus models that intertwine the 4D‐QSAR and molecular docking methods contribute to more reliable virtual screening and compound optimization experiments. Additionally, the use of multi‐objective modeling schemes permits the simultaneous evaluation of different chemical and biological profiles, which should contribute to the control a priori of causative factors for the high attrition rates in later drug discovery phases.|
|Appears in Collections:||DQI - Artigos publicados em periódicos|
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