Network Pharmacology Integrated Molecular Docking to Reveal the Autism and Mechanism of Baohewan Heshiwei Wen Dan Tang

Study of PARP inhibitors for breast cancer based on enhanced multiple kernel function SVR with PSO

PARP1 is one of six enzymes required for the highly error-prone DNA repair pathway microhomology-mediated end joining (MMEJ) and needs to be inhibited when over-expressed. In order to study the PARP1 inhibitory effect of fused tetracyclic or pentacyclic dihydrodiazepinoindolone derivatives (FTPDDs) by quantitative structure-activity relationship technique, six models were established by four kinds of methods, heuristic method, gene expression programming, random forester, and support vector regression with single, double, and triple kernel function respectively. The single, double, and triple kernel functions were RBF kernel function, the integration of RBF and polynomial kernel functions, and the integration of RBF, polynomial, and linear kernel functions respectively. The problem of multi-parameter optimization introduced in the support vector regression model was solved by the particle swarm optimization algorithm. Among the models, the model established by support vector regression with triple kernel function, in which the optimal R 2 and RMSE of training set and test set were 0.9353, 0.9348 and 0.0157, 0.0288, and R2 cv of training set and test set were 0.9090 and 0.8971, shows the strongest prediction ability and robustness. The method of support vector regression with triple kernel function is a great promotion in the field of quantitative structure-activity relationship, which will contribute a lot to designing and screening new drug molecules. The information contained in the model can provide important factors that guide drug design. Based on these factors, six new FTPDDs have been designed. Using molecular docking experiments to determine the properties of new derivatives, the new drug was ultimately successfully designed.

Exploring the mechanism of Astragali radix for promoting osteogenic differentiation based on network pharmacology, molecular docking, and experimental validation

The present study used network pharmacology and molecular docking to predict the active ingredients and mechanisms of action of Astragalus radix (AR) to promote osteogenic differentiation of bone marrow mesenchymal stem cells (BM-MSCs), and cell experiments were conducted for verification. First, network pharmacology was used to predict the effective components, targets, and mechanisms of action of AR to promote osteogenic differentiation. The effective components and corresponding target proteins of AR, and the target proteins of osteogenic differentiation were collected through the database. The intersection targets of the two were used for the construction and analysis of a protein-protein interaction (PPI) network. Gene Oncology (GO) and Kyoto Encyclopedia of Genes, and Genomes (KEGG) enrichment analyses were conducted. Next, molecular docking technology was carried out to verify the interaction between the active ingredient and the target protein, and to select the appropriate effective active ingredient. Finally, the results of network pharmacology analysis were verified by in vitro experiments. A total of 95 potential targets were retrieved by searching the intersection of AR and osteogenic differentiation targets. PPI network analysis indicated that RAC-α-serine-threonine-protein kinase (Akt1) was considered to be the most reliable target for AR to regulate osteogenic differentiation. GO enrichment analysis included 21 biological processes, 21 cellular components and 100 molecular functions. KEGG enrichment analysis indicated that the class I phosphatidylinositol-3 kinase (PI3K)-serine-threonine kinase (Akt) signaling pathway may play an important role in promoting osteogenic differentiation. The results of molecular docking showed that quercetin's performance was improved compared with that of kaempferol. In vitro experiments showed that quercetin promoted the expression of osteogenic marker proteins (including collagen I, Runt-related transcription factor 2 and osteopontin) in BMSCs and activated the PI3K/Akt signaling pathway. AR acted on Akt1 targets through its main active component quercetin, and promoted the osteogenic differentiation of BM-MSCs by activating the PI3K/Akt signaling pathway.

Exploring the shared gene signatures of smoking-related osteoporosis and chronic obstructive pulmonary disease using machine learning algorithms

Objectives: Cigarette smoking has been recognized as a predisposing factor for both osteoporosis (OP) and chronic obstructive pulmonary disease (COPD). This study aimed to investigate the shared gene signatures affected by cigarette smoking in OP and COPD through gene expression profiling. Materials and methods: Microarray datasets (GSE11784, GSE13850, GSE10006, and GSE103174) were obtained from Gene Expression Omnibus (GEO) and analyzed for differentially expressed genes (DEGs) and weighted gene co-expression network analysis (WGCNA). Least absolute shrinkage and selection operator (LASSO) regression method and a random forest (RF) machine learning algorithm were used to identify candidate biomarkers. The diagnostic value of the method was assessed using logistic regression and receiver operating characteristic (ROC) curve analysis. Finally, immune cell infiltration was analyzed to identify dysregulated immune cells in cigarette smoking-induced COPD. Results: In the smoking-related OP and COPD datasets, 2858 and 280 DEGs were identified, respectively. WGCNA revealed 982 genes strongly correlated with smoking-related OP, of which 32 overlapped with the hub genes of COPD. Gene Ontology (GO) enrichment analysis showed that the overlapping genes were enriched in the immune system category. Using LASSO regression and RF machine learning, six candidate genes were identified, and a logistic regression model was constructed, which had high diagnostic values for both the training set and external validation datasets. The area under the curves (AUCs) were 0.83 and 0.99, respectively. Immune cell infiltration analysis revealed dysregulation in several immune cells, and six immune-associated genes were identified for smoking-related OP and COPD, namely, mucosa-associated lymphoid tissue lymphoma translocation protein 1 (MALT1), tissue-type plasminogen activator (PLAT), sodium channel 1 subunit alpha (SCNN1A), sine oculis homeobox 3 (SIX3), sperm-associated antigen 9 (SPAG9), and vacuolar protein sorting 35 (VPS35). Conclusion: The findings suggest that immune cell infiltration profiles play a significant role in the shared pathogenesis of smoking-related OP and COPD. The results could provide valuable insights for developing novel therapeutic strategies for managing these disorders, as well as shedding light on their pathogenesis.

2D, 3D-QSAR study and docking of vascular endothelial growth factor receptor 3 (VEGFR3) inhibitors for potential treatment of retinoblastoma

Background: Retinoblastoma is currently the most common malignant tumor seen in newborns and children's eyes worldwide, posing a life-threatening hazard. Chemotherapy is an integral part of retinoblastoma treatment. However, the chemotherapeutic agents used in clinics often lead to drug resistance. Thus there is a need to investigate new chemotherapy-targeted agents. VEGFR3 inhibitors are anti-tumour-growth and could be used to develop novel retinoblastoma-targeted agents. Objective: To predict drug activity, discover influencing factors and design new drugs by building 2D, 3D-QSAR models. Method: First, linear and non-linear QSAR models were built using heuristic methods and gene expression programming (GEP). The comparative molecular similarity indices analysis (COMISA) was then used to construct 3D-QSAR models through the SYBYL software. New drugs were designed by changing drug activity factors in both models, and molecular docking experiments were performed. Result: The best linear model created using HM had an R², S², and R²cv of 0.82, 0.02, and 0.77, respectively. For the training and test sets, the best non-linear model created using GEP had correlation coefficients of 0.83 and 0.72 with mean errors of 0.02 and 0.04. The 3D model designed using SYBYL passed external validation due to its high Q² (0.503), R² (0.805), and F-value (76.52), as well as its low standard error of SEE value (0.172). This demonstrates the model's reliability and excellent predictive ability. Based on the molecular descriptors of the 2D model and the contour plots of the 3D model, we designed 100 new compounds using the best active compound 14 as a template. We performed activity prediction and molecular docking experiments on them, in which compound 14.d performed best regarding combined drug activity and docking ability. Conclusion: The non-linear model created using GEP was more stable and had a more substantial predictive power than the linear model built using the heuristic technique (HM). The compound 14.d designed in this experiment has the potential for anti-retinoblastoma treatment, which provides new design ideas and directions for retinoblastoma-targeted drugs.