Diabetes mellitus (DM) is a complex metabolic disorder characterized by chronic hyperglycemia, with aldose reductase playing a critical role in the pathophysiology of diabetic complications. This study aimed to investigate the efficacy of flavonoid compounds as potential aldose reductase inhibitors using a combination of molecular docking and molecular dynamics (MD) simulations. The three-dimensional structures of representative flavonoid compounds were obtained from PubChem, minimized, and docked against aldose reductase using Discovery Studio’s CDocker module. The top 10 compounds Daidzein, Quercetin, Kaempferol, Butin, Genistein, Sterubin, Baicalein, Pulchellidin, Wogonin, and Biochanin_A were selected based on their lowest docking energy values for further analysis. Subsequent MD simulations over 100 ns revealed that Daidzein and Quercetin maintained the highest stability, forming multiple conventional hydrogen bonds and strong hydrophobic interactions, consistent with their favorable interaction energies and stable RMSD values. Comparative analysis of hydrogen bond interactions and RMSD profiles underscored the ligand stability. MMPBSA analysis further confirmed the significant binding affinities of Daidzein and Quercetin, highlighting their potential as aldose reductase inhibitors. This study highlights the potential of flavonoids as aldose reductase inhibitors, offering insights into their binding interactions and stability, which could contribute to developing novel therapeutics for DM complications.

Despite their clonal origins, tumors eventually develop into complex communities made up of phenotypically different cell subpopulations, according to mounting evidence. Tumor cell-intrinsic programming and signals from geographically and temporally changing microenvironments both contribute to this variability. Furthermore, the mutational load is typically lacking in childhood malignancies of adult cancers, and they still exhibit high cellular heterogeneity levels largely mediated by epigenetic mechanisms. Ewing sarcomas represent highly aggressive malignancies affecting both bone and soft tissue, primarily afflicting adolescents. Unfortunately, the outlook for patients facing relapsed or metastatic disease is grim. These tumors are primarily fueled by a distinctive fusion event involving an FET protein and an ETS family transcription factor, with the most prevalent fusion being EWS/FLI1. Despite originating from a common driver mutation, Ewing sarcoma cells display significant variations in transcriptional activity, both within and among tumors. Recent research has pinpointed distinct fusion protein activities as a principal source of this heterogeneity, resulting in markedly diverse cellular phenotypes. In this review, we aim to characterize the role of the EWS/FLI fusion protein in Ewing sarcoma by exploring its general mechanism of activation and elucidating its implications for tumor heterogeneity. Additionally, we delve into potential therapeutic opportunities to target this aberrant fusion protein in the context of Ewing sarcoma treatment.

Ewing sarcoma (ES) is a highly malignant carcinoma prevalent in children and most frequent in the second decade of life. It mostly occurs due to t(11;22) (q24;q12) translocation. This translocation encodes the oncogenic fusion protein EWS/FLI (Friend leukemia integration 1 transcription factor), which acts as an aberrant transcription factor to deregulate target genes essential for cancer. Traditionally, flavonoids from plants have been investigated against viral and cancerous diseases and have shown some promising results to combat these disorders. In the current study, representative flavonoid compounds from various subclasses are selected and used to disrupt the RNA-binding motif of EWS, which is required for EWS/FLI fusion. By blocking the RNA-binding motif of EWS, it might be possible to combat ES. Therefore, molecular docking experiments validated the binding interaction patterns and structural behaviors of screened flavonoid compounds within the active region of the Ewing sarcoma protein (EWS). Furthermore, pharmacogenomics analysis was used to investigate potential drug interactions with Ewing sarcoma-associated genes. Finally, molecular dynamics simulations were used to investigate the stability of the best selected docked complexes. Taken together, daidzein, kaempferol, and genistein exhibited a result comparable to ifosfamide in the proposed in silico study and can be further analyzed as possible candidate compounds in biological in vitro studies against ES.

γ-Aminobutyric acid aminotransferase (GABA-AT) is a pyridoxal 5′-phosphate (PLP)-dependent enzyme that degrades γ-aminobutyric (GABA) in the brain. GABA is an important inhibitory neurotransmitter that plays important neurological roles in the brain. Therefore, GABA-AT is an important drug target that regulates GABA levels. Novel and potent drug development to inhibit GABA-AT is still a very challenging task. In this study, we aimed to devise novel and potent inhibitors against GABA-AT using computer-aided drug design (CADD) tools. Since the crystal structure of human GABA-AT was not yet available, we utilized a homologous structure derived from our previously published paper. To identify highly potent compounds relative to vigabatrin, an FDA-approved drug against human GABA-AT, we developed a pharmacophore analysis protocol for 530,000 Korea Chemical Bank (KCB) compounds and selected the top 50 compounds for further screening. Preliminary biological analysis was carried out for these 50 compounds and 16 compounds were further assessed. Subsequently, molecular docking, molecular dynamics (MD) simulations, and binding free energy calculations were carried out. In the results, four predicted compounds, A07, B07, D08, and H08, were found to be highly potent and were further evaluated by a biological activity assay to confirm the results of the GABA-AT activity inhibition assay.

The increasing utilization of artificial intelligence algorithms in drug development has proven to be highly efficient and effective. One area where deep learning-based approaches have made significant contributions is in drug repositioning, enabling the identification of new therapeutic applications for existing drugs. In the present study, a trained deep-learning model was employed to screen a library of FDA-approved drugs to discover novel inhibitors targeting JAK2. To accomplish this, reference datasets containing active and decoy compounds specific to JAK2 were obtained from the DUD-E database. RDKit, a cheminformatic toolkit, was utilized to extract molecular features from the compounds. The DeepChem framework’s GraphConvMol, based on graph convolutional network models, was applied to build a predictive model using the DUD-E datasets. Subsequently, the trained deep-learning model was used to predict the JAK2 inhibitory potential of FDA-approved drugs. Based on these predictions, ribociclib, topiroxostat, amodiaquine, and gefitinib were identified as potential JAK2 inhibitors. Notably, several known JAK2 inhibitors demonstrated high potential according to the prediction results, validating the reliability of our prediction model. To further validate these findings and confirm their JAK2 inhibitory activity, molecular docking experiments were conducted using tofacitinib—an FDA-approved drug for JAK2 inhibition. Experimental validation successfully confirmed our computational analysis results by demonstrating that these novel drugs exhibited comparable inhibitory activity against JAK2 compared to tofacitinib. In conclusion, our study highlights how deep learning models can significantly enhance virtual screening efforts in drug discovery by efficiently identifying potential candidates for specific targets such as JAK2. These newly discovered drugs hold promises as novel JAK2 inhibitors deserving further exploration and investigation.

Malaria remains a global health challenge, with increasing resistance to frontline antimalarial treatments such as artemisinin (ART) threatening the efficacy of current therapies. In this study, we investigated the potential of FDA-approved drugs to selectively inhibit the malarial proteasome, a novel target for antimalarial drug development. By leveraging pharmacophore modeling, molecular docking, molecular dynamics (MD) simulations, and binding free-energy calculations, we screened a library of compounds to identify inhibitors selective for the Plasmodium proteasome over the human proteasome. Our results highlighted Argatroban, LM-3632, Atazanavir Sulfate, and Pemetrexed Hydrate as promising candidates, with Argatroban and Pemetrexed Hydrate showing the highest binding affinity and selectivity toward the malarial proteasome. MD simulation and gmx_MMPBSA analysis confirmed the compounds’ ability to remain within the active site of the malarial proteasome, while some exited or exhibited reduced stability within the human proteasome. This study underscores the potential of proteasome-targeting drugs for overcoming malarial drug resistance and paves the way for the further optimization of these compounds.