Bisphenol A (BPA), an environmental endocrine disruptor, is implicated in hepatocellular carcinoma (HCC), but its molecular mechanisms are unclear. This study employed an integrative computational framework to identify potential BPA-related molecular targets in HCC, assess their statistical clinical value, and generate hypotheses regarding their roles within the tumor microenvironment. BPA and HCC targets were retrieved from public databases and intersected with differentially expressed genes in HCC, identifying fifteen overlapping genes statistically enriched in cell cycle regulation, p53 signaling, and viral carcinogenesis. Six hub genes (MKI67, CCNA2, EZH2, CCNB1, CDK1, BIRC5) were significantly upregulated in HCC with high internal cross-validated diagnostic accuracy (AUC > 0.96), although these estimates may be susceptible to overfitting and require external validation. Molecular docking and dynamics simulations predicted stable BPA binding to six proteins (Ki67, Cyclin A2, EZH2, Cyclin B1, CDK1, Survivin), with van der Waals forces calculated as the primary driving energy contribution by MM-PBSA. The two-gene (CCNB1/EZH2) risk model showed statistical associations with patient survival, validated internally and externally, although its generalizability remains limited. Mendelian randomization provided genetic evidence consistent with a potential risk-associated role for CCNB1 and a protective-associated role for EZH2. Single-cell analysis localized high CCNB1 and EZH2 expression to malignant and proliferative T-cells, correlating with specific immune infiltration patterns and checkpoint expression. In conclusion, these computational findings suggest a statistical and structural association between BPA exposure and HCC-related core cell-cycle regulators (e.g., CCNB1/EZH2). The data generate the hypothesis that CCNB1 and EZH2 may serve as prognostic biomarkers and potential contributors to HCC biology, possibly through coordinated effects on cell cycle dysregulation and immune microenvironment remodeling, though direct evidence of in vivo molecular targeting by BPA or causal pathway activation is not established by this study. These findings provide novel insights into BPA's putative role in hepatocarcinogenesis and offer clues for future experimental validation regarding risk assessment and therapeutic strategies.

Wound healing is a multifactorial biological process that requires the coordinated regulation of inflammation, cell migration, angiogenesis, growth, and extracellular matrix remodeling. These drawbacks of existing wound care treatments prompt the need to seek safe and cost-effective agents and multi-target agents. This was demonstrated in a study that investigated the wound-healing capacity of a natural flavonoid glycoside, rutin, using an integrated in vitro-in silico methodology. An MTT assay was used to determine the cytocompatibility of rutin in L929 fibroblast cells, and the results showed that cell viability was high at a wide range of concentrations. A scratch wound healing assay showed enhanced fibroblast migration and wound closure in a concentration-dependent manner, and wound contraction was nearly complete within 72h at optimum concentrations. To understand the molecular mechanisms underlying these effects, network pharmacological analysis revealed 94 common targets between rutin-associated wound healing and rutin-associated genes. Protein-protein interaction analysis identified key regulatory nodes, such as PIK3R1, PRKCA, and EGFR, which are central to the pathways that regulate cell proliferation, migration, angiogenesis, and inflammatory regulation. Enrichment analysis of gene ontology and KEGG pathways revealed that the PI3K-Akt, MAPK, VEGF, and AGE-RAGE signaling pathways were highly involved. Molecular docking showed good binding affinities of rutin to PIK3R1, PRKCA, and EGFR, which was also confirmed by 100 ns molecular dynamics simulations that revealed complex stability and favorable conformational behavior. Density functional theory analysis showed that the electronic characteristics were in line with the antioxidant activity and the strong intermolecular interactions. Together, these results demonstrate that rutin is a promising multifunctional topical wound-healing agent and provide mechanistic evidence for its use in topical therapeutic applications.

μ-Conotoxins target voltage-gated sodium channels, with KIIIA, CnIIIC, and SxIIIC showing notable inhibition of NaV1.7, which is strongly implicated in pain signaling. This study focuses on three such peptides-KIIIA, CnIIIC, and SxIIIC-and investigates their potential as analgesic according to the elucidation of their selectivity mechanism against different channel subtypes. Using computational simulations and pharmacological assays, we identified the critical role of the basic residues at the C-terminal of μ-conotoxin in binding to NaV channels. Two engineered mutants, R10EKIIIA and R20WCnIIIC, demonstrated significantly enhanced analgesic activity in vivo compared to their wild-type toxins. These findings highlight the potential of rational μ-conotoxin engineering for developing effective and safer pain therapeutics.

Accurate prediction of drug metabolism and pharmacokinetics (ADMET) properties is crucial in drug discovery. Here, we present a novel approach to enhance ADMET property predictions using Cross-Aligned Multimodal Attention (CMA) mechanisms, pretrained models, and multimodal techniques. ADMET data is collected and processed using image processing, graph neural networks, and chemical fingerprinting. Pretrained models like GROVER and ResNet generate a multi-channel data format, and the CMA mechanism aligns and correlates the data modalities. Grad-CAM technology interprets the model's predictions, visually demonstrating the relationship between compound properties and fragments. Our ADMET property prediction server ( http://guolab.mpu.edu.mo/CMA ) implements the CMA-based model and a substantial language model for ADMET property prediction. The innovation lies in the integration of multimodal data, the application of pretrained models, and the development of cross-modal alignment. This approach improves the efficiency and accuracy of ADMET property predictions and opens new avenues for research in molecular science, particularly in drug design and evaluation.

An efficient protocol for the synthesis of aryl thiocyanates from anilines and KSCN by merging nitrate reduction with copper-catalysis has been developed. The combination of inexpensive Fe(NO3)3·9H2O and Na2S2O3·5H2O enabled a safe and green diazotization of anilines. The fleeting diazonium salts reacted with KSCN rapidly to afford the aryl thiocyanates in good to excellent yields using Cu(OTf)2 as the catalyst. The operational simplicity and safety, good substrate scope and scalability highlight the synthetic significance of this protocol.

Diabetes is a chronic medical disorder caused by insufficient production of the hormone insulin by the pancreas. Although there are various treatment options available for controlling diabetes, including non-peptide-based medications, the majority of these have adverse effects and are limited in comparison to peptide-based drugs. Protein drugs offer numerous benefits, including weight loss, significant reductions in blood glucose levels, and an extremely low risk of hypoglycemia. This article discusses treatment modalities, presents existing therapies, provides an in-depth comparison of peptide-based and other drugs, examines current development and barriers, offers some recommendations, and outlines future research directions for peptide drugs in the treatment of T2DM. In recent days, several computational tools and AI models, including ESMFold, ProteinMPNN, Schrödinger, and AutoDock Vina, have played an essential role in peptide-based drug discovery. Therefore, this article also highlights the significance of AI drug discovery, diverse AI models, and other computational tools to enhance peptide-based drug discovery and development.