Human soluble epoxide hydrolase (sEH), a dual-functioning homodimeric enzyme with hydrolase and phosphatase activities, is known for its pivotal role in the hydrolysis of epoxyeicosatrienoic acids. Inhibitors targeting sEH have shown promising potential in the treatment of various life-threatening diseases. In this study, we employed a range of in silico modeling approaches to investigate a diverse dataset of structurally distinct sEH inhibitors. Our primary aim was to develop predictive and validated models while gaining insights into the structural requirements necessary for achieving higher inhibitory potential. To accomplish this, we initially calculated molecular descriptors using nine different descriptor-calculating tools, coupled with stochastic and non-stochastic feature selection strategies, to identify the most statistically significant linear 2D-QSAR model. The resulting model highlighted the critical roles played by topological characteristics, 2D pharmacophore features, and specific physicochemical properties in enhancing inhibitory potential. In addition to conventional 2D-QSAR modeling, we implemented the Transformer-CNN methodology to develop QSAR models, enabling us to obtain structural interpretations based on the Layer-wise Relevance Propagation (LRP) algorithm. Moreover, a comprehensive 3D-QSAR analysis provided additional insights into the structural requirements of these compounds as potent sEH inhibitors. To validate the findings from the QSAR modeling studies, we performed molecular dynamics (MD) simulations using selected compounds from the dataset. The simulation results offered crucial insights into receptor–ligand interactions, supporting the predictions obtained from the QSAR models. Collectively, our work serves as an essential guideline for the rational design of novel sEH inhibitors with enhanced therapeutic potential. Importantly, all the in silico studies were performed using open-access tools to ensure reproducibility and accessibility.

RNA-dependent RNA polymerase (RdRp) is a potential therapeutic target for the discovery of novel antiviral agents for the treatment of life-threatening infections caused by newly emerged strains of the influenza virus. Being one of the most conserved enzymes among RNA viruses, RdRp and its inhibitors require further investigations to design novel antiviral agents. In this work, we systematically investigated the structural requirements for antiviral properties of some recently reported aryl benzoyl hydrazide derivatives through a range of in silico tools such as 2D-quantitative structure-activity relationship (2D-QSAR), 3D-QSAR, structure-based pharmacophore modeling, molecular docking and molecular dynamics simulations. The 2D-QSAR models developed in the current work achieved high statistical reliability and simultaneously afforded in-depth mechanistic interpretability towards structural requirements. The structure-based pharmacophore model developed with the docked conformation of one of the most potent compounds with the RdRp protein of H5N1 influenza strain was utilized for developing a 3D-QSAR model with satisfactory statistical quality validating both the docking and the pharmacophore modeling methodologies performed in this work. However, it is the atom-based alignment of the compounds that afforded the most statistically reliable 3D-QSAR model, the results of which provided mechanistic interpretations consistent with the 2D-QSAR results. Additionally, molecular dynamics simulations performed with the apoprotein as well as the docked complex of RdRp revealed the dynamic stability of the ligand at the proposed binding site of the receptor. At the same time, it also supported the mechanistic interpretations drawn from 2D-, 3D-QSAR and pharmacophore modeling. The present study, performed mostly with open-source tools and webservers, returns important guidelines for research aimed at the future design and development of novel anti-viral agents against various RNA viruses like influenza virus, human immunodeficiency virus-1, hepatitis C virus, corona virus, and so forth.

Cinnamoyl sucrose esters (CSEs) were evaluated as AGIs and their enzyme inhibition activity and potency were compared with gold standard acarbose. The inhibition activity of the CSEs against α-glucosidase and α-amylase depended on their structure including the number of the cinnamoyl moieties, their position, and the presence or absence of the acetyl moieties. The inhibitory values of the CSEs 2–9 generally increases in the order of mono-cinnamoyl moieties < di-cinnamoyl ≤ tri-cinnamoyl < tetra-cinnamoyl. This trend was supported from both in vitro and in silico results. Both tetra-cinnamoyl CSEs 5 and 9 showed the highest α-glucosidase inhibitory activities of 77 ± 5%, 74 ± 9%, respectively, against acarbose at 27 ± 4%, and highest α-amylase inhibitory activities of 98 ± 2%, 99 ± 1%, respectively, against acarbose at 93 ± 2%. CSEs 3, 4, 6, 7, 8 showed desired higher inhibition of α-glucosidase than α-amylase suggesting potential for further development as AGIs with reduced side effects. Molecular docking studies on CSEs 5 and 9 attributed the high inhibition of these compounds to multiple π-π interactions and favorable projection of the cinnamoyl moieties (especially O-3 cinnamoyl) in the enzyme pockets. This work proposes CSEs as new AGIs with potentially reduced side effects.

Aims: Cuscuta reflexa Roxb. has been traditionally utilized as an ethnomedicinal agent for managing various ailments, including diabetes, jaundice, and rheumatic issues. This investigation aimed to evaluate the oral hypoglycemic and hypolipidemic effects of the hydroalcoholic extract of the aerial parts of Cuscuta reflexa (HAECR) in rats with diabetes induced by a high-fat diet (HFD) and streptozotocin (STZ). Additionally, the study sought to identify the bioactive compounds responsible for these effects in Cuscuta reflexa. Background: To date, only a few attempts have been made to isolate the lead bioactive compound from Cuscuta reflexa, highlighting the need for further exploration of this herb through bioactivityguided fractionation. The isolation and characterization of the responsible lead molecules could pave the way for developing a new, safer therapeutic option compared to conventionally used drugs. Objectives: The study aims to evaluate the oral hypoglycemic and hypolipidemic activity of hydroalcoholic extract of the aerial parts of Cuscuta reflexa (HAECR) in a high-fat diet (HFD) and streptozotocin (STZ) induced diabetic rats, along with elucidating the responsible bioactive compounds of Cuscuta reflexa. Methods: The hydroalcoholic plant extract was standardized by marker calibration by HPLC and, subsequently, phytochemical screening by LC-MS. Column chromatography was adopted to isolate bioactive compounds from the extract, which NMR, FT-IR, and MS spectroscopy subsequently characterized. Oral antihyperglycemic activity and toxicity studies were performed, and antioxidant activities were studied. Results: No deaths or behavioural changes were observed during the toxicity study up to 2000 mg/kg oral dosing. Other toxicological parameters, such as deviations in organ weights, and haematological, and histological parameters, were also not observed. 250 mg/kg dose of HAECR exhibited significant antihyperglycemic activity (56.9% reduction in blood glucose level) in diabetic rats. In the HAECR-treated groups, significant reductions were observed compared to the control group, with cholesterol levels decreasing by 57%, triglycerides by 50%, SGPT by 50%, and SGOT by 30%. Considering the extensive analytical spectroscopy interpretation results, Quercetin and Stigmasterol are the two possible isolated compounds from our extract. Conclusion: Thus, this study suggested the antihyperlipidemic and antihyperglycemic activity of HAECR, which may be attributed to phytomolecules such as Stigmasterol and Quercetin. Therefore, our findings from the investigated part of the herb could be regarded as an adjunctive agent for treating diabetes mellitus.

Liver X Receptors (LXRs) are members of the nuclear receptor family. These play crucial role in lipid and cholesterol metabolism and considered as key regulators of numerous inflammatory pathways. LXRα is considered as an important biological target for metabolic, cardiovascular, inflammatory, and neoplastic diseases. In the current work, we performed machine learning-based cheminformatic modelling with a curated dataset containing LXRα ligands, collected from ChEMBL database and explored four different methodologies including (a) molecular fingerprint-based modelling, (b) Dragon descriptor-based modelling, (c) 3D-MIL-QSAR and (d) Transformer-CNN technique. For each of these, multiple ML methods were employed and all models were generated in a competitive manner in order to obtain the most predictive model. A support vector machine-based model generated with FCFP4 molecular fingerprint afforded the best model with greater than 85% balanced accuracy against training set. After performing additional external validation based on maximum voting with duplicated datapoints and establishing clear applicability domain, this model was made publicly available through Streamlit-based application named ’LXRα PREDICTOR’, which allows the users to obtain predictivity of compound(s) provided as SMILES notation(s) and clearly visualize the contour map exhibiting favourable and unfavourable regions in the molecule for increased potential towards LXRα. Additionally, this application may be run in standalone mode from Github repository. This work explores both conventional and advanced or recently proposed cheminformatic modelling techniques and tally their performance in handling large, complex and inhomogeneous datasets. At the same time, it improves the scope for new ligand identification or design for LXRα ligands for the treatment of life-threatening diseases.