Diabetic nephropathy (DN) is a major worldwide cause of chronic kidney disease and end-stage renal failure, impacting approximately 20-50% of individuals with diabetes. DN frequently progresses to end-stage renal disease. The prevalence of DN continues to increase significantly, accompanied by rising rates of death and cardiovascular complications. To date, there is no specific drug approved exclusively for the treatment of diabetic nephropathy. In this work 13 novel disubstituted TQ analogues were synthesized, recrystallized and structurally verified via microanalytical and spectral data. Screening against TLR-4 activity was performed, the most potent disubstituted propyl ether TQ13 was subjected to more in-vivo biological investigations in STZ induced hyperglycemia rats. TQ13 showed anti-hyperglycemic, antioxidant, anti-inflammatory and antiapoptotic effects in addition to amelioration of kidney function markers, TLR-2 & TLR-4 activities. The findings of this investigational study suggest that disubstituted propyl ether TQ13 may represent a promising radio-stable renoprotective candidate in DN therapeutics which needs more tentative studies on toxicity and exact mechanism of action involving advanced staging pathways.

The human intestinal peptide transporter 1 (SLC15A1, PepT1) is a critical determinant of oral drug bioavailability, yet its thermodynamic characterization and structural tractability remain challenging. While bacterial orthologues like the Escherichia coli DtpA transporter offer highly stable structural surrogates, significant cross-species variations in the binding microenvironment limit their pharmacological fidelity. Traditional structure-based engineering aimed at engrafting the human pharmacophore onto bacterial scaffolds is frequently hindered by combinatorial explosion and severe thermodynamic frustration, as non-native side-chains disrupt co-evolved local packing. To bypass the empirical bottlenecks of in vitro directed evolution, we present a fully in silico co-evolutionary pipeline leveraging deep contextual protein language models (PLMs). By mapping 19 pharmacologically critical human PepT1 residues onto the DtpA sequence, we utilized the 650-million parameter ESM-2 model to conduct zero-shot mutational profiling. We identified 14 primary humanizing mutations to which the model assigned very low likelihoods (below 10 - 5). To resolve these energetic conflicts, we deployed an automated epistatic rescue algorithm to sweep the flanking microenvironmental topologies. This computationally efficient heuristic successfully identified localized, non-native compensatory mutations for all high-risk targets without relying on traditional molecular dynamics. Most notably, the highly deleterious Q41R and R305F substitutions were buffered by Y38D and I304K secondary mutations, yielding 5,215-fold and 11,614-fold increases in the model likelihood assigned to the primary substitution, respectively; these are changes in sequence likelihood and not calculated free energies. Binding dynamics were assessed via docking calculations and all-atom membranous molecular dynamics simulations. This deep learning-driven framework computationally rationalizes the engraftment of the human binding pocket, reducing the potential experimental screening space and yielding a humanized surrogate that requires experimental validation of folding, binding and transport before use in pharmacological screening.

This current study employs structure-guided virtual screening to identify potential inhibitors from 69,075 natural product or natural product-like compounds in the InterBioscreen (IBS) database against the SARS-CoV-2 Main protease (Mpro), a well-validated target. From high-throughput structure-guided virtual screening, subsequently followed by more rigorous re-docking, top seven compounds were chosen as hits based on their high binding affinity and specific H-bond interactions with the active-site residues of Mpro. All these compounds were new for this target as well as the virus. The stability of the seven protein-ligand complexes was further assessed using molecular dynamics (MD) simulations and the MM-GBSA method. The ADME/Tox profile of these seven compounds was also predicted theoretically. After MD simulation, only three compounds, viz. STOCK1N-86169, STOCK1N-88750, and STOCK1N-65657 were identified as high-potential candidates for Mpro inhibition. To confirm the in silico analysis results, an in vitro FRET-based Mpro inhibition assay and an ELISA-based SARS-CoV-2 inhibition assay were performed. From these in vitro investigations, STOCK1N-86169 was identified as the only potent Mpro inhibitor, exhibiting an IC50 of 3.8 µM. Independent confirmation of STOCK1N-86169 binding to Mpro was obtained by differential scanning calorimetry (DSC), which revealed an increase of approximately 15 °C in the melting temperature (Tm) of the Mpro-STOCK1N-86169 complex relative to the apo-protein. In the cytotoxicity assay, it showed CC50 > 100 µM for HeLa, ~ 100 µM for the Huh-7 cell line, and ~ 33.5 µM for the Vero E6 cell line. In the Vero cell-based SARS-CoV-2 infection assay, it showed an EC50 of ~ 7 µM, with a reasonable Selective Index (S.I.) of ~ 5-fold. Our study suggests that the natural product like compound STOCK1N-86169, which presents a new scaffold for Mpro inhibition and also SARS-CoV-2, may be a promising candidate for further development as an effective inhibitor of SARS-CoV-2 Mpro.

A series of targeted chalcones (TCs) was designed and synthesised using a microwave-assisted synthetic method to introduce diverse substituents at different positions on the phenyl ring. The structures of all nineteen TC analogues were confirmed by NMR, FTIR, and HRMS. In vitro tests against acetyl- and butyrylcholinesterases (AChE and BuChE), β-Site Amyloid Precursor Protein Cleaving Enzyme 1 (BACE1), and monoamine oxidases (MAO-A and MAO-B) showed that MAO-B was the most potent and selective. Among them, TC12 was the strongest inhibitor (IC50 = 11.00 ± 1.0 nM; SI > 3636) and worked competitively (Ki = 7.16 ± 1.20 nM). In terms of IC50 = 50.0 ± 1.0 nM and SI > 800, TC9 was ranked second. TC12 showed no time-dependent inhibition of MAO-B, and its MAO-B inhibition was reversible. Molecular docking studies revealed that TC12 establishes favourable interactions within the MAO-B active site, particularly through π-π stacking with aromatic cage residues (TYR398 and TYR435), while showing limited engagement with key regions of MAO-A. ADME predictions indicated that TC12 exhibits favourable drug-like properties, including compliance with Lipinski's rules, moderate CNS permeability, and a pharmacokinetic profile supportive of oral bioavailability. In vitro Parallel Artificial Membrane Permeability Assay (PAMPA) and microsomal stability studies revealed that TC9 exhibited high blood-brain barrier (BBB) permeability comparable to carbamazepine, while TC12 showed moderate permeability but was consistent with its predicted CNS penetration. Both TC analogues demonstrated favourable metabolic stability profiles in rat and human liver microsomes, supporting their potential for further pharmacological evaluation. Overall, TC12 represents a promising candidate for selective MAO-B inhibition.

Benzo[a]pyrene (BaP) is a polycyclic fragrant hydrocarbon contaminant commonly establish throughout the surroundings. The International Agency for Research on Cancer has classified it as a Group 1 carcinogen; however, its exact contribution to the onset of prostate cancer (PCa) is still not well defined. This study systematically explores the mechanism underlying the association between BaP exposure and prostate cancer using approaches including network toxicology, machine learning, transcriptomic validation, immune infiltration assessment, single-cell analysis, molecular docking and external validation. The results showed that BaP and prostate cancer shared 975 overlapping targets, which were predominantly enriched in signaling pathways such as PI3K-Akt. Four core genes were screened out through differential analysis and machine learning, namely CAV1, TWIST1, PRKCA, and GDF15. Transcriptome verification showed that TWIST1 and GDF15 were up-regulated, CAV1 and PRKCA were down-regulated, and the AUC of the four-gene combined diagnosis reached 0.990. Core genes are associated with cellular growth and immune cell recruitment, and single-cell sequencing verified their specific cellular distribution and immunological shifts. Molecular docking showed that BaP binds well to the core target. External validation confirmed that BaP may promotes the progression of PCa, with upregulated expression of GDF15 and downregulated expression of PRKCA in PCa. In conclusion, BaP may promote the development of PCa by regulating pathways such as CAV1, TWIST1, GDF15 and PRKCA.

Cardiovascular disease remains a leading cause of mortality worldwide, and macrophage-derived foam-cell formation represents an important event in atherosclerosis. VAV2, a Rho-family guanine nucleotide-exchange factor, participates in signalling pathways associated with oxidized low-density lipoprotein uptake and macrophage lipid accumulation. In this study, a structure-based drug-repurposing workflow was used to identify clinically relevant compounds with predicted affinity for a full-length VAV2 structural model. Virtual screening, molecular docking, ADMET prediction, three independent 200-ns molecular-dynamics simulations, principal-component analysis, and MM-PBSA calculations prioritized talazoparib, tivozanib, and isradipine for further evaluation. In THP-1-derived macrophages, talazoparib reduced oxLDL-induced intracellular neutral-lipid accumulation at a sub-cytotoxic concentration. In a complementary DiI-oxLDL assay, all three compounds reduced cell-associated DiI-oxLDL fluorescence relative to vehicle control. These findings provide preliminary computational and cellular support for further investigation of the selected compounds. However, direct binding to or inhibition of VAV2 has not been demonstrated and requires biochemical, genetic, pharmacological, and in vivo validation.

Lipases are key enzymes in lipid metabolism and represent important therapeutic targets for obesity and metabolic disorders. This study integrates sequence analysis, structural modelling, molecular docking, molecular dynamics simulations, and enzymatic assays to elucidate evolutionary conservation and the inhibitory mechanism of orlistat against lipase ITB 2.1. Multiple sequence alignment revealed strong conservation of catalytic serine residue at the nucleophilic elbow across bacterial, fungal, and mammalian lipases, with rare AXSXG motif substitutions in Bacillus and Geobacillus species, indicating adaptive structural variation. Phylogenetic and distance analyses demonstrated clear evolutionary clustering while revealing unexpected relationships among mammalian, fungal, and bacterial lipases, suggesting functional convergence. Superimposition of 3-dimensional structure confirmed preservation of the α/β-hydrolase fold and highlighted lid-dependent conformational changes that regulate active-site accessibility. Molecular docking showed substantially stronger binding of orlistat to the open-lid conformation than to the closed state, with lipase ITB 2.1 exhibiting the most favourable binding affinity among the analysed enzymes. Molecular dynamics simulations (100 ns) confirmed stable ligand-enzyme complexes, with monoacylglycerol lipase and pancreatic lipase displaying the highest structural stability, while lipase ITB 2.1 maintained consistent compactness and ligand retention. Enzymatic inhibition assays experimentally validated the computational predictions, demonstrating kinetic value with Vmax 1428 µM/min; Km 81 µM and complete inhibition at 56.25 µM orlistat. Collectively, these findings establish lipase ITB 2.1 as a robust model for lipase inhibition and drug screening studies.

Glioblastoma (GBM) is the most lethal primary brain malignancy, for which no new systemic therapy has achieved regulatory approval in over past decade. A molecular vulnerability of GBM is its comprehensive co-dependency on exogenous cholesterol to sustain oncogenic signaling and membrane biogenesis, rendering the liver X receptor beta (LXRβ)-the principal transcriptional regulator of cholesterol efflux in the central nervous system-an attractive therapeutic target. However, the development of LXRβ-selective, blood-brain barrier (BBB)-penetrant agonists that avoid the hepatotoxicity associated with LXRα co-activation has remained an unresolved challenge. Here, we developed an integrated artificial intelligence-based virtual screening pipeline. The pipeline integrates ML-QSAR modelling, D-MPNN, and deep learning-based DTI prediction. It was applied to systematically interrogate a curated library of ~ 1.2 million natural products. Sequential filtering by predicted LXRβ affinity, LXRβ/LXRα selectivity, and integrated pharmacokinetic scoring, refined by molecular docking and MM-GBSA binding free energy calculations, identified four high-confidence candidates. Experimental validation by CCK-8 cytotoxicity assays across five GBM cell lines and six normal cell models revealed that 5,6-Epoxyergosterol is a potent anti-glioma agent. Furthermore, a 200 ns molecular dynamics simulation of the LXRβ-5,6-Epoxyergosterol complex demonstrated stable binding, with dominant hydrogen-bonding contacts at His435 and Trp443 and a free energy landscape consistent with a single dominant agonist-bound conformation. These findings establish fungal ergosterol derivatives as a novel class of LXRβ-targeted therapeutics and provide a validated AI-driven framework for accelerating natural product drug discovery in neuro-oncology.

Few-shot molecular property prediction aims to adapt to new property tasks using only a few labeled molecules. However, existing methods are still limited by insufficient molecular representations and biased intra-task relation construction under scarce supervision. To address these limitations, this paper proposes HD-SKRG, a hierarchical dual-view and structure-knowledge relation graph enhancement network. HD-SKRG improves few-shot prediction from two complementary aspects: molecular representation learning and intra-task relation propagation. For molecular representation, HD-SKRG builds atom-level and functional-group-level graphs, injects elemental knowledge into atom representations, and transfers local atomic information to functional-group representations. A frequency-aware aggregation module further produces molecular-level knowledge representations. For intra-task relation propagation, the structure relation graph provides the main feature-propagation pathway, whereas the knowledge relation graph supplies semantically related neighbors and refines relation weights. This design reduces graph-construction bias caused by relying on structural similarity alone. The dual-view encoders are pretrained by cross-view contrastive learning, and the full model is meta-trained under the MAML framework. Experiments on Tox21, SIDER, MUV, and ToxCast under 1-shot and 10-shot settings show that, HD-SKRG achieves the best results in five of eight settings and the second-best results in the remaining three. Ablation studies further confirm the contribution of each module.

An NH4I-mediated electrochemical three-component cascade reaction has been established for the preparation of trideuteromethylthiolated enaminones from primary amines, acetylacetone, and CD3SSO3Na. This simple protocol is readily applicable to gram-scale synthesis and late-stage modification of bioactive molecules. Preliminary biological screening of the prepared trideuteromethylthiolated enaminones identified compound 4l is capable of protecting cells from oxidative damage by initiating the activation of the Nrf2/HO-1 signaling pathway.

Targeted protein degradation (TPD) has revolutionized drug discovery, with PROTACs leading the charge by catalytically eliminating disease-causing proteins. While conventional PROTACs degrade a single target, the complexity of diseases like cancer and neurodegeneration-marked by redundant and compensatory signaling networks-has spurred the rapid development of dual/multi-target PROTACs capable of simultaneously degrading two or more pathogenic proteins. This review systematically surveys recent advances (2023-2026) in this area, categorizing them into two classes: those targeting homologous proteins (e.g., CDKs, BCL-2/BCL-xL, HDACs, BAZ2A/BAZ2B) and those targeting distinct proteins across interconnected pathways (e.g., ERα/ARO, α-Syn/tau, BET/HDAC, PI3K/mTOR, FLT3/CHK1, CBP/BRD4, and others). For each, we critically analyze design strategies, structure-activity relationships, linker optimization, and E3 ligase selection. Despite remarkable progress-including the first dual degraders for non-kinase epigenetic regulators and protein aggregates-challenges persist in pharmacokinetics, off-target toxicity, and limited E3 ligase diversity. By consolidating key breakthroughs and practical SAR insights, this review provides a valuable resource for advancing next-generation multi-target degraders toward clinical translation for complex diseases.

Human rhinoviruses (HRVs) are major causative agents of the common cold and are strongly associated with asthma exacerbation, highlighting the need for effective antivirals. Because the viral 3 C protease (3Cpro) is essential for polyprotein processing and is highly conserved among HRV species, it represents an attractive therapeutic target. In this study, we performed structure-based virtual screening of 49,437 compounds against HRV-C 3Cpro using AutoDock Vina, followed by ADMET-based prioritization of the top hits. Docking analysis identified two lead compounds that favorably occupied the catalytic pocket and formed stabilizing interactions with key residues, including His40, Glu71, and Cys147, in comparison with the reference inhibitor rupintrivir. In 200-ns molecular dynamics simulations, both compounds stabilized the protease relative to the apo form, with compound 1 showing the lowest protein RMSD (1.21 ± 0.22 Å) and compound 2 producing the lowest Cys147 RMSF (0.48 Å). Both ligands also displayed lower ligand RMSD values than rupintrivir (compound 1: 1.49 ± 0.78 Å; compound 2: 2.01 ± 0.39 Å; rupintrivir: 4.83 ± 0.89 Å), indicating more persistent binding within the active site. MM/PBSA analysis further supported stronger effective binding for compound 1 (-23.59 ± 6.87 kcal/mol) and compound 2 (-25.25 ± 5.75 kcal/mol) than for rupintrivir (-19.43 ± 4.46 kcal/mol). Taken together, these results identify compounds 1 and 2 as promising non-covalent HRV 3Cpro inhibitor candidates for further experimental validation.

Matrix metalloproteinase-9 (MMP-9) plays a pivotal role in extracellular matrix remodelling and is strongly implicated in tumour progression, metastasis, and angiogenesis, making it an attractive therapeutic target for anticancer drug discovery. However, the development of potent and selective MMP-9 inhibitors has been significantly hampered due to the high structural homology among members of the MMP family, particularly within the catalytic domain. Structural studies revealed a non-conserved S1' pocket of MMP-9 exhibits distinct structural variability compared to other MMP isoforms, providing an opportunity to achieve improved selectivity. To the best of our knowledge, fragment-based drug design (FBDD) approaches specifically targeting the S1' pocket of MMP-9 have not been systematically explored. In the present study, an integrated in silico FBDD workflow was employed to identify and optimize small molecules occupying the S1' pocket of MMP-9. Initial fragment-based virtual screening identified low-molecular-weight fragments with moderate binding affinity toward the target pocket. A promising fragment hit was subsequently optimised through a receptor cavity-guided fragment-growing strategy to extend interactions toward residues in the neighbouring S2' pocket. Induced-fit docking analysis identified several optimized molecules with enhanced binding affinity and favourable orientation within the S1' pocket. The stability of the protein-hit molecule complexes was further validated through 200 ns molecular dynamics simulations, which confirmed stable binding interactions for four molecules. Comparative docking and MM-GBSA analyses against MMP isoforms demonstrated a preferential affinity of hit molecules for MMP-9, suggesting a potential selectivity profile driven by ligand binding free energy and orientation in S1' pocket. In addition, in silico ADME profiling indicated favourable pharmacokinetic properties of all the stable hit molecules, while density functional theory studies helped to gain insights into the electronic distribution and reactivity of the molecules. Collectively, this study establishes an in silico framework for the structure-based design of potentially selective MMP-9 inhibitors targeting the non-conserved S1' pocket.

Aberrant activation of the Wnt/β-catenin signaling pathway is a hallmark of colorectal cancer, primarily driven by the interaction between β-catenin and T-cell factor 4 (Tcf4). Due to the large and shallow interface (~ 4800 Å[2]) of this protein-protein interaction, it has historically been considered "undruggable." However, emerging evidence highlights the presence of druggable hot spots within this interface. In this study, we employed a structure-based virtual screening approach to repurpose FDA-approved drugs targeting the β-catenin/Tcf4 interaction. Using physics-based molecular simulations and MM/GBSA binding free energy calculations, we identified Leucovorin (Folinic acid), Carbenicillin, and Ceforanide as promising candidate hit compounds with stable binding to critical hot spot residues (Asn430, Lys435, His470, Arg474, and Lys508) of β-catenin. Experimental validation using HT-29 colorectal cancer cells revealed that Carbenicillin and Ceforanide reduced the cell viability, impaired the colony formation, and exhibited superior anti-proliferative activity compared to the other candidates. These findings provide compelling evidence for the feasibility of targeting β-catenin/Tcf4 interactions using small molecules and suggest that Carbenicillin and Ceforanide may serve as promising repurposed candidates for therapeutic intervention in Wnt-driven malignancies.