Learning from Multiple Classifier Systems: Perspectives for Improving Decision Making of QSAR Models in Medicinal Chemistry

Beyond model interpretability using LDA and decision trees for α-amylase and α-glucosidase inhibitor classification studies

In this report are used two data sets involving the main antidiabetic enzyme targets α-amylase and α-glucosidase. The prediction of α-amylase and α-glucosidase inhibitory activity as antidiabetic is carried out using LDA and classification trees (CT). A large data set of 640 compounds for α-amylase and 1546 compounds in the case of α-glucosidase are selected to develop the tree model. In the case of CT-J48 have the better classification model performances for both targets with values above 80%-90% for the training and prediction sets, correspondingly. The best model shows an accuracy higher than 95% for training set; the model was also validated using 10-fold cross-validation procedure and through a test set achieving accuracy values of 85.32% and 86.80%, correspondingly. Additionally, the obtained model is compared with other approaches previously published in the international literature showing better results. Finally, we can say that the present results provided a double-target approach for increasing the estimation of antidiabetic chemicals identification aimed by double-way workflow in virtual screening pipelines.

In Silico Assessment of ADME Properties: Advances in Caco-2 Cell Monolayer Permeability Modeling

One of the main goals of in silico Caco-2 cell permeability models is to identify those drug substances with high intestinal absorption in human (HIA). For more than a decade, several in silico Caco-2 models have been made, applying a wide range of modeling techniques; nevertheless, their capacity for intestinal absorption extrapolation is still doubtful. There are three main problems related to the modest capacity of obtained models, including the existence of inter- and/or intra-laboratory variability of recollected data, the influence of the metabolism mechanism, and the inconsistent in vitro-in vivo correlation (IVIVC) of Caco-2 cell permeability. This review paper intends to sum up the recent advances and limitations of current modeling approaches, and revealed some possible solutions to improve the applicability of in silico Caco-2 permeability models for absorption property profiling, taking into account the above-mentioned issues.

Quinazolin-4(3H)-one-Based Hydroxamic Acids: Design, Synthesis and Evaluation of Histone Deacetylase Inhibitory Effects and Cytotoxicity

The present article describes the synthesis and biological activity of various series of novel hydroxamic acids incorporating quinazolin-4(3H)-ones as novel small molecules targeting histone deacetylases. Biological evaluation showed that these hydroxamic acids were potently cytotoxic against three human cancer cell lines (SW620, colon; PC-3, prostate; NCI-H23, lung). Most compounds displayed superior cytotoxicity than SAHA (suberoylanilide hydroxamic acid, Vorinostat) in term of cytotoxicity. Especially, N-hydroxy-7-(7-methyl-4-oxoquinazolin-3(4H)-yl)heptanamide (5b) and N-hydroxy-7-(6-methyl-4-oxoquinazolin-3(4H)-yl)heptanamide (5c) (IC₅₀ values, 0.10-0.16 μm) were found to be approximately 30-fold more cytotoxic than SAHA (IC₅₀ values of 3.29-3.67 μm). N-Hydroxy-7-(4-oxoquinazolin-3(4H)-yl)heptanamide (5a; IC₅₀ values of 0.21-0.38 μm) was approximately 10- to 15-fold more potent than SAHA in cytotoxicity assay. These compounds also showed comparable HDAC inhibition potency with IC₅₀ values in sub-micromolar ranges. Molecular docking experiments indicated that most compounds, as represented by 5b and 5c, strictly bound to HDAC2 at the active binding site with binding affinities much higher than that of SAHA.