Prediction of the relationship between the structural features of andrographolide derivatives and α-glucosidase inhibitory activity: A quantitative structure-activity relationship (QSAR) Study

Artificial intelligence in antidiabetic drug discovery: The advances in QSAR and the prediction of α-glucosidase inhibitors

Artificial Intelligence is transforming drug discovery, particularly in the hit identification phase of therapeutic compounds. One tool that has been instrumental in this transformation is Quantitative Structure-Activity Relationship (QSAR) analysis. This computer-aided drug design tool uses machine learning to predict the biological activity of new compounds based on the numerical representation of chemical structures against various biological targets. With diabetes mellitus becoming a significant health challenge in recent times, there is intense research interest in modulating antidiabetic drug targets. α-Glucosidase is an antidiabetic target that has gained attention due to its ability to suppress postprandial hyperglycaemia, a key contributor to diabetic complications. This review explored a detailed approach to developing QSAR models, focusing on strategies for generating input variables (molecular descriptors) and computational approaches ranging from classical machine learning algorithms to modern deep learning algorithms. We also highlighted studies that have used these approaches to develop predictive models for α-glucosidase inhibitors to modulate this critical antidiabetic drug target.

Neuroprotective Effect for Cerebral Ischemia by Natural Products: A Review

Natural products have a significant role in the prevention of disease and boosting of health in humans and animals. Stroke is a disease with high prevalence and incidence, the pathogenesis is a complex cascade reaction. In recent years, it’s reported that a vast number of natural products have demonstrated beneficial effects on stroke worldwide. Natural products have been discovered to modulate activities with multiple targets and signaling pathways to exert neuroprotection via direct or indirect effects on enzymes, such as kinases, regulatory receptors, and proteins. This review provides a comprehensive summary of the established pharmacological effects and multiple target mechanisms of natural products for cerebral ischemic injury in vitro and in vivo preclinical models, and their potential neuro-therapeutic applications. In addition, the biological activity of natural products is closely related to their structure, and the structure-activity relationship of most natural products in neuroprotection is lacking, which should be further explored in future. Overall, we stress on natural products for their role in neuroprotection, and this wide band of pharmacological or biological activities has made them suitable candidates for the treatment of stroke.

Synthesis, extracorporeal nephrotoxicity, and 3D-QSAR of andrographolide derivatives

Andrographolide is a traditional Chinese medicine monomer with many pharmacological activities, but has potential nephrotoxicity. Here, we aim to investigate the relationship between modification of andrographolide structure and its nephrotoxicity. Twenty-three andrographolide derivatives were synthesized, and their structures were confirmed by ¹ H-NMR and HRMS. Nephrotoxicity of these compounds against human renal tubular epithelial (HK-2) cells was evaluated using the MTT assay. The results indicated that most of them had significantly reduced nephrotoxicity, especially compounds III, V, and IX_c , with IC₅₀ values of 1,985, 1,300, and 806.9 μmol/L, respectively, which were obviously superior to andrographolide (IC₅₀ 30.60 μmol/L). However, compounds I_a -I_f (IC₅₀ values < 30 μmol/L), the 14-OH derivatives of andrographolide, showed higher nephrotoxicity than that of andrographolide. Three-dimensional quantitative structure-activity relationship (3D-QSAR) models of COMFA and COMSIA were established (COMFA: q²  = 0.639, r²  = 0.951; COMSIA: q²  = 0.569, r²  = 0.857). This model allowed proposing five new compounds with lower theoretical nephrotoxicity, which would be worthwhile to synthesize and evaluate. We believe that predicted models will help us to understand the structural modification requirements of andrographolide to reduce the nephrotoxicity, and further investigations will be needed to determine the mechanism involved in the effect of less nephrotoxic compounds.

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.

Natural products as inhibitors of Leishmania major dihydroorotate dehydrogenase

The flavoenzyme dihydroorotate dehydrogenase (DHODH) catalyzes the fourth reaction of the de novo pyrimidine biosynthetic pathway, which exerts vital functions in the cells, especially within DNA and RNA biosynthesis. Thus, this enzyme stands out as a new key molecular target for parasites causing Neglected Diseases (NDs). Focused on contributing to the development of new therapeutic alternatives for NDs, in this study, for the first time, a screening of 57 natural products for in vitro inhibition of Leishmania major DHODH (LmDHODH) was carried out, including cross validation against the human DHODH (HsDHODH). A subset of natural products consisting of 21 sesquiterpene lactones (STLs) was submitted to QSAR studies. Additionally, thermostability studies by differential scanning fluorimetry (DSF) were performed to determine whether the STLs are effectively or not binding to the enzyme. The IC₅₀ values against LmDHODH varied from 27 to 1200 μM; only irrelevant inhibition was obtained on HsDHODH. DSF assays confirmed binding of STLs to LmDHODH; moreover, it is suggested that such inhibitors might act in a different site other than the active site. A reliable QSAR model based on molecular descriptors was obtained (R²: 0.83; Q²_CV: 0.69 and Q²_EXT/F2: 0.66) indicating that stronger inhibition requires a balanced distribution of the hydrophobic regions across the molecular surface, as well as higher width and lower hydrophobicity of the molecules. A pharmacophore-based 3D-QSAR approach also afforded a useful model (R²: 0.72; Q²_CV: 0.50 and Q²_EXT/F2: 0.62), which confirmed the importance of proper orientation of the ligands, molecular surface features and shape for stronger inhibition, reflecting properties of a putative common binding site. These data indicated for the first time that natural products can actually inhibit LmDHODH and highlighted some metabolites as potentially interesting starting points for the discovery of more potent LmDHODH inhibitors, ultimately aiming at new effective therapeutic alternatives for leishmaniasis and, possibly, other NDs caused by trypanosomatids.

Comparative structural analysis of α-glucosidase inhibitors on difference species: a computational study

Structural feature analysis of chlorogenic acid derivatives made up of varying lengths of alkyl groups as α-glucosidases inhibitors were performed by QSAR techniques. The statistically significant models derived from the study were validated by leave one out, Y-randomization and test set methods. The predictive capacity of the models was assessed by its validation parameters such as crossvalidated correlation coefficients (Q(2)), predictive residual analysis and other correlation parameters. The results obtained from the study show that the models were constructed with vsurf like properties (vsurf_ID4, vsurf_ID7 and vsurf_CW8), partial charge (Q_VSA_FNEG) and conformation dependent charged (dipoleX) descriptors. The integy moments of hydrophobicity descriptors (ID4 and ID7) are contributed for the inhibitory activity of the α-glucosidases enzymes of both the species. The vsurf_ID7 descriptor has contributed significantly (negatively) for the inhibitory activity prediction of α-glucosidases enzymes of S. cerevisiae. The partial negative charge on the surface of the molecules is detrimental for the activity, which reveals that the active site of the enzymes may have negatively charged groups. The pharmacophore analysis results also confirm the presence of hydrophilic properties on the vdW surface of the molecules. These results explain that the active sites of α-glucosidase enzymes of both the species have the same environment for the interaction. The alkyl side chain on the molecules is important for the pharmacokinetic behavior of the molecules and reduces the interaction energy of the molecules with the water. Hence, these results will be useful for designing novel molecules with multiple activities.

In Silico–Based Structural Analysis of Arylthiophene Derivatives for FTase Inhibitory Activity, hERG, and Other Toxic Effects

In the present investigation, the authors have performed an in silico–based analysis on a series of arylthiophene derivatives for the determination of their structural features responsible for farnesyltransferase (FTase) inhibitory activity, hERG blocking activity, and toxicity by quantitative structure–activity relationship and pharmacophore analysis techniques. The statistically significant models derived through multiple linear regression analysis were validated by different validation methods. The applicability of the descriptors contributed in the selected models show that the polar and polarizable properties on the van der Waals (vdW) surface area of the molecules are important for the FTase inhibitory and hERG blocking activities, while being detrimental for the toxicity of the molecules. It is interesting to note that the topological properties, molecular flexibility, and connectivity of the molecules are positively correlated to all the activities (FTase inhibition, hERG blocking, and toxicity). This implies that the flexibility of the molecules is the common feature for interaction in all targets, whereas the presence of polar groups on the molecular surface (vdW) is a determinant for the favorable (FTase inhibition) or unwanted effect (hERG blocking and toxicity) of the molecules. The pharmacophore analysis of the molecules demonstrated that the aromatic/hydrophobicity and polarizability features are important pharmacophore contours favorable for these activities.