Quinoxalinones Based Aldose Reductase Inhibitors: 2D and 3D-QSAR Analysis

e-QSAR (Explainable AI-QSAR), molecular docking, and ADMET analysis of structurally diverse GSK3-beta modulators to identify concealed modulatory features vindicated by X-ray

Glycogen Synthase Kinase-3 beta (GSK-3β) is a crucial enzyme linked to various cellular processes, including neurodegeneration, autophagy, and diabetes. A structurally diverse set of 1293 molecules having GSK-3β modulatory activity has been used. Molecular docking and eXplainable Artificial Intelligence (XAI) have been used concomitantly. The approach involves using GA for feature selection and XGBoost for in-depth analysis, yielding strong statistical validation with R2tr = 0.9075, R2L10 %O = 0.9116, and Q2F3 = 0.7841. Molecular docking provided complementary and similar results. Machine learning model interpretation using SHapley Additive exPlanations (SHAP) revealed that specific structural features like aromatic carbon with specific partial charges, non-ring nitrogen atoms, sp3-hybrid carbon atoms, and the topological distance between carbon and nitrogen atoms, among others, significantly influence the modulatory profile. The results are also supported by reported X-ray resolved structures. In addition, in-silico ADMET analysis is also accomplished. This research underscores the value of advanced machine learning techniques in understanding complex biological phenomena and supporting rational drug design.

CADD Studies in the Discovery of Potential ARI (Aldose Reductase Inhibitors) Agents for the Treatment of Diabetic Complications

The lack of currently available drugs for treating diabetes complications has stimulated our interest in finding new Aldose Reductase inhibitors (ARIs) with more beneficial biological properties. One metabolic method uses aldose reductase inhibitors in the first step of the polyol pathway to control excess glucose flux in diabetic tissues. Computer-aided drug discovery (CADD) is key in finding and optimizing potential lead substances. AR inhibitors (ARI) have been widely discussed in the literature. For example, Epalrestat is currently the only ARI used to treat patients with diabetic neuropathy in Japan, India, and China. Inhibiting R in patients with severe to moderate diabetic autonomic neuropathy benefits heart rate variability. AT-001, an AR inhibitor, is now being tested in COVID-19 to see how safe and effective it reduces inflammation and cardiac damage. In summary, these results from animal and human studies strongly indicate that AR can cause cardiovascular complications in diabetes. The current multi-center, large-scale randomized human study of the newly developed powerful ARI may prove its role in diabetic cardiovascular disease to establish therapeutic potential. During the recent coronavirus disease (COVID-19) outbreak in 2019, diabetes and cardiovascular disease were risk factors for severely negative clinical outcomes in patients with COVID19. New data shows that diabetes and obesity are among the strongest predictors of COVID-19 hospitalization. Patients and risk factors for severe morbidity and mortality of COVID- 19.

QSAR, pharmacophore modeling and molecular docking studies to identify structural alerts for some nitrogen heterocycles as dual inhibitor of telomerase reverse transcriptase and human telomeric G-quadruplex DNA

Background

Telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA are amongst the favorable target for researchers to discover novel and more effective anticancer agents. To understand and elucidate structure activity relationship and mechanism of inhibition of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA, a QSAR modeling and molecular docking were conducted.

Results

Two robust QSAR model were obtained which consist of full set QSAR model (R2: 0.8174, CCCtr: 0.8995, Q2loo: 0.7881, Q2LMO: 0.7814) and divided set QSAR model (R2: 0.8217, CCCtr: 0.9021, Q2loo: 0.7886, Q2LMO: 0.7783, Q2-F1: 0.7078, Q2-F2: 0.6865, Q2-F3: 0.7346) for envisaging the inhibitory activity of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA. The analysis reveals that carbon atom exactly at 3 bonds from aromatic carbon atom, nitrogen atom exactly at six bonds from planer nitrogen atom, aromatic carbon atom within 2 A0 from the center of mass of molecule and occurrence of element hydrogen within 2 A0 from donar atom are the key pharmacophoric features important for dual inhibition of TERT and human telomeric G-quadruplex DNA. To validate this analysis, pharmacophore modeling and the molecular docking is performed. Molecular docking analysis support QSAR analysis and revealed that, dual inhibition of TERT and human telomeric DNA is mainly contributed from hydrophobic and hydrogen bonding interactions.

Conclusion

The findings of molecular docking, pharmacophore modelling, and QSAR are all consistent and in strong agreement. The validated QSAR analyses can detect structural alerts, pharmacophore modelling can classify a molecule's consensus pharmacophore involving hydrophobic and acceptor regions, whereas docking analysis can reveal the mechanism of dual inhibition of telomerase reverse transcriptase (TERT) and human telomeric G-quadruplex DNA. The combination of QSAR, pharmacophore modeling and molecular docking may be useful for the future drug design of dual inhibitors to combat the devastating issue of resistance.

Graphical abstract

Identification of concealed structural alerts using QSTR modeling for Pseudokirchneriella subcapitata

In the present work, QSTR modeling was conducted for microalga Pseudokirchneriella subcapitata using a data set of 271 molecules belonging to different types of chemical classes for the prediction of EC₅₀ for 72 hr based assays. The balanced QSTR model encompasses seven easily interpretable molecular descriptors and possesses statistical robustness with high predictive ability. This Genetic Algorithm Multi-linear regression (GA-MLR) model was subjected to internal validation, Y-randomization test, applicability domain analysis, and external validation as per the recommended OECD guidelines. The newly developed model fulfilled the threshold values for more than 20 recommended validation parameters including R² = 0.72, Q²_LOO = 0.70, etc. The developed QSTR model was successful in identifying the type of hybridization or specific type of atoms of previously reported and newer structural alerts. Thus, the model could be useful for data gap filling and expanding mechanistic interpretation of toxicity for different chemicals.

Development of Aldose Reductase Inhibitors for the Treatment of Inflammatory Disorders and Cancer: Current Drug Design Strategies and Future Directions

Aldose Reductase (AR) is an enzyme that converts glucose to sorbitol during the polyol pathway of glucose metabolism. AR has been shown to be involved in the development of secondary diabetic complications due to its involvement in causing osmotic as well as oxidative stress. Various AR inhibitors have been tested for their use to treat secondary diabetic complications, such as retinopathy, neuropathy, and nephropathy in clinical studies. Recent studies also suggest the potential role of AR in mediating various inflammatory complications. Therefore, the studies on the development and potential use of AR inhibitors to treat inflammatory complications and cancer besides diabetes are currently on the rise. Further, genetic mutagenesis studies, computer modeling, and molecular dynamics studies have helped design novel and potent AR inhibitors. This review discussed the potential new therapeutic use of AR inhibitors in targeting inflammatory disorders and cancer besides diabetic complications. Further, we summarized studies on how AR inhibitors have been designed and developed for therapeutic purposes in the last few decades.

Identification of Anti-SARS-CoV-2 Compounds from Food Using QSAR-Based Virtual Screening, Molecular Docking, and Molecular Dynamics Simulation Analysis

Due to the genetic similarity between SARS-CoV-2 and SARS-CoV, the present work endeavored to derive a balanced Quantitative Structure-Activity Relationship (QSAR) model, molecular docking, and molecular dynamics (MD) simulation studies to identify novel molecules having inhibitory potential against the main protease (Mpro) of SARS-CoV-2. The QSAR analysis developed on multivariate GA-MLR (Genetic Algorithm-Multilinear Regression) model with acceptable statistical performance (R2 = 0.898, Q2loo = 0.859, etc.). QSAR analysis attributed the good correlation with different types of atoms like non-ring Carbons and Nitrogens, amide Nitrogen, sp2-hybridized Carbons, etc. Thus, the QSAR model has a good balance of qualitative and quantitative requirements (balanced QSAR model) and satisfies the Organisation for Economic Co-operation and Development (OECD) guidelines. After that, a QSAR-based virtual screening of 26,467 food compounds and 360 heterocyclic variants of molecule 1 (benzotriazole-indole hybrid molecule) helped to identify promising hits. Furthermore, the molecular docking and molecular dynamics (MD) simulations of Mpro with molecule 1 recognized the structural motifs with significant stability. Molecular docking and QSAR provided consensus and complementary results. The validated analyses are capable of optimizing a drug/lead candidate for better inhibitory activity against the main protease of SARS-CoV-2.

QSAR and Pharmacophore Modeling of Nitrogen Heterocycles as Potent Human N-Myristoyltransferase (Hs-NMT) Inhibitors

N-myristoyltransferase (NMT) is an important eukaryotic monomeric enzyme which has emerged as an attractive target for developing a drug for cancer, leishmaniasis, ischemia-reperfusion injury, malaria, inflammation, etc. In the present work, statistically robust machine leaning models (QSAR (Quantitative Structure–Activity Relationship) approach) for Human NMT (Hs-NMT) inhibitory has been performed for a dataset of 309 Nitrogen heterocycles screened for NMT inhibitory activity. Hundreds of QSAR models were derived. Of these, the model 1 and 2 were chosen as they not only fulfil the recommended values for a good number of validation parameters (e.g., R² = 0.77–0.79, Q²_LMO = 0.75–0.76, CCC_ex = 0.86–0.87, Q²-F³ = 0.74–0.76, etc.) but also provide useful insights into the structural features that sway the Hs-NMT inhibitory activity of Nitrogen heterocycles. That is, they have an acceptable equipoise of descriptive and predictive qualities as per Organisation for Economic Co-operation and Development (OECD) guidelines. The developed QSAR models identified a good number of molecular descriptors like solvent accessible surface area of all atoms having specific partial charge, absolute surface area of Carbon atoms, etc. as important features to be considered in future optimizations. In addition, pharmacophore modeling has been performed to get additional insight into the pharmacophoric features, which provided additional results.

Structure features of peptide-type SARS-CoV main protease inhibitors: Quantitative structure activity relationship study

In the present work, an extensive QSAR (Quantitative Structure Activity Relationships) analysis of a series of peptide-type SARS-CoV main protease (MPro) inhibitors following the OECD guidelines has been accomplished. The analysis was aimed to identify salient and concealed structural features that govern the MPro inhibitory activity of peptide-type compounds. The QSAR analysis is based on a dataset of sixty-two peptide-type compounds which resulted in the generation of statistically robust and highly predictive multiple models. All the developed models were validated extensively and satisfy the threshold values for many statistical parameters (for e.g. R² ​= ​0.80-0.82, Q² _loo ​= ​0.74-0.77, Q ² _LMO  ​= ​0.66-0.67). The developed QSAR models identified number of sp² hybridized Oxygen atoms within seven bonds from aromatic Carbon atoms, the presence of Carbon and Nitrogen atoms at a topological distance of 3 and other interrelations of atom pairs as important pharmacophoric features. Hence, the present QSAR models have a good balance of Qualitative (Descriptive QSARs) and Quantitative (Predictive QSARs) approaches, therefore useful for future modifications of peptide-type compounds for anti- SARS-CoV activity.