Current computer-aided drug design最新文献

Computer-aided molecular modeling is a rapidly emerging technology that is being used to accelerate the discovery and design of new drug therapies. It involves the use of computer algorithms and 3D structures of molecules to predict interactions between molecules and their behavior in the body. This has drastically improved the speed and accuracy of drug discovery and design. Additionally, computer-aided molecular modeling has the potential to reduce costs, increase the quality of data, and identify promising targets for drug development. Through the use of sophisticated methods, such as virtual screening, molecular docking, pharmacophore modeling, and quantitative structure-activity relationships, scientists can achieve higher levels of efficacy and safety for new drugs. Moreover, it can be used to understand the activity of known drugs and simplify the process of formulating, optimizing, and predicting the pharmacokinetics of new and existing drugs. In conclusion, computer-aided molecular modeling is an effective tool to rapidly progress drug discovery and design by predicting the interactions between molecules and anticipating the behavior of new drugs in the body.

Background: In this study, we aimed to develop a new end-to-end learning model called Graph-Drug-Target Interaction (DTI), which integrates various types of information in the heterogeneous network data, and to explore automatic learning of the topology-maintaining representations of drugs and targets, thereby effectively contributing to the prediction of DTI. Precise predictions of DTI can guide drug discovery and development. Most machine learning algorithms integrate multiple data sources and combine them with common embedding methods. However, the relationship between the drugs and target proteins is not well reported. Although some existing studies have used heterogeneous network graphs for DTI prediction, there are many limitations in the neighborhood information between the nodes in the heterogeneous network graphs. We studied the drug-drug interaction (DDI) and DTI from DrugBank Version 3.0, protein-protein interaction (PPI) from the human protein reference database Release 9, drug structure similarity from Morgan fingerprints of radius 2 and calculated by RDKit, and protein sequence similarity from Smith-Waterman score.

Methods: Our study consists of three major components. First, various drugs and target proteins were integrated, and a heterogeneous network was established based on a series of data sets. Second, the graph neural networks-inspired graph auto-encoding method was used to extract high-order structural information from the heterogeneous networks, thereby revealing the description of nodes (drugs and proteins) and their topological neighbors. Finally, potential DTI prediction was made, and the obtained samples were sent to the classifier for secondary classification.

Results: The performance of Graph-DTI and all baseline methods was evaluated using the sums of the area under the precision-recall curve (AUPR) and the area under the receiver operating characteristic curve (AUC). The results indicated that Graph-DTI outperformed the baseline methods in both performance results.

Conclusion: Compared with other baseline DTI prediction methods, the results showed that Graph-DTI had better prediction performance. Additionally, in this study, we effectively classified drugs corresponding to different targets and vice versa. The above findings showed that Graph-DTI provided a powerful tool for drug research, development, and repositioning. Graph- DTI can serve as a drug development and repositioning tool more effectively than previous studies that did not use heterogeneous network graph embedding.

Aims and objectives: Metabolic syndrome (MetS) is a group of metabolic disorders that includes obesity in combination with at least any two of the following conditions, i.e., insulin resistance, high blood pressure, low HDL cholesterol, and high triglycerides level. Treatment of this syndrome is challenging because of the multiple interlinked factors that lead to increased risks of type-2 diabetes and cardiovascular diseases. This study aims to conduct extensive in silico analysis to (i) find central genes that play a pivotal role in MetS and (ii) propose suitable drugs for therapy. Our objective is to first create a drug-disease network and then identify novel genes in the drug-disease network with strong associations to drug targets, which can help in increasing the therapeutical effects of different drugs. In the future, these novel genes can be used to calculate drug synergy and propose new drugs for the effective treatment of MetS.

Methods: For this purpose, we (i) investigated associated drugs and pathways for MetS, (ii) employed eight different similarity measures to construct eight gene regulatory networks, (iii) chose an optimal network, where a maximum number of drug targets were central, (iv) determined central genes exhibiting strong associations with these drug targets and associated disease-causing pathways, and lastly (v) employed these candidate genes to propose suitable drugs.

Results: Our results indicated (i) a novel drug-disease network complex, with (ii) novel genes associated with MetS.

Conclusion: Our developed drug-disease network complex closely represents MetS with associated novel findings and markers for an improved understanding of the disease and suggested therapy.

Background: In vitro, the molecular docking method has been suggested for estimating the biological affinity of the pharmacophores with physiologically active compounds. It is the latter stage in molecular docking, and the docking scores are examined using the AutoDock 4.2 tool program. The chosen compounds can be evaluated for in vitro activity based on the binding scores, and the IC₅₀ values can be computed.

Objective: The purpose of this work was to create methyl isatin compounds as potential antidepressants, compute physicochemical characteristics, and carry out docking analysis.

Methods: The protein data bank of the RCSB (Research Collaboratory for Structural Bioinformatics) was used to download the PDB structures of monoamine oxidase (PDB ID: 2BXR) and indoleamine 2,3-dioxygenase (PDB ID: 6E35). Based on the literature, methyl isatin derivatives were chosen as the lead chemicals. By determining their IC₅₀ values, the chosen compounds were tested for in vitro anti-depressant activity.

Results: The binding scores for the interactions of SDI 1 and SD 2 with indoleamine 2,3 dioxygenase were found to be -10.55 kcal/mol and -11.08 kcal/mol, respectively, while the scores for their interactions with monoamine oxidase were found to be -8.76 kcal/mol and -9.28 kcal/mol, respectively, using AutoDock 4.2. The relationship between biological affinity and pharmacophore electrical structure was examined using the docking technique. The chosen compounds were tested for their ability to inhibit MAO, and the IC₅₀ values for each were found to be 51.20 and 56, respectively.

Conclusion: This investigation has identified many novel and effective MAO-A inhibitors from the family of chemicals known as methyl isatin derivatives. Lead optimization was applied to the SDI 1 and SDI 2 derivatives. The superior bioactivity, pharmacokinetic profile, BBB penetration, pre-ADMET profiles, such as HIA (human intestinal absorption) and MDCK (Madin-Darby canine kidney), plasma protein binding, toxicity assessment, and docking outcomes, have been obtained. According to the study, synthesised isatin 1 and SDI 2 derivatives exhibited a stronger MAO inhibitory activity and effective binding energy, which may help prevent stress-induced depression and other neurodegenerative disorders caused by a monoamine imbalance.

Background: Diffuse large B-cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma worldwide. Novel treatment strategies are still needed for this disease.

Objective: The present study aimed to systematically explore the potential targets and molecular mechanisms of matrine in the treatment of DLBCL.

Methods: Potential matrine targets were collected from multiple platforms. Microarray data and clinical characteristics of DLBCL were downloaded from publicly available database. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were applied to identify the hub genes of DLBCL using R software. Then, the shared target genes between matrine and DLBCL were identified as the potential targets of matrine against DLBCL. The least absolute shrinkage and selection operator (LASSO) algorithm was used to determine the final core target genes, which were further verified by molecular docking simulation and receiver operating characteristic (ROC) curve analysis. Functional analysis was also performed to elucidate the potential mechanisms.

Results: A total of 222 matrine target genes and 1269 DLBCL hub genes were obtained through multiple databases and machine learning algorithms. From the nine shared target genes of matrine and DLBCL, five final core target genes, including CTSL, NR1H2, PDPK1, MDM2, and JAK3, were identified. Molecular docking showed that the binding of matrine to the core genes was stable. ROC curves also suggested close associations between the core genes and DLBCL. Additionally, functional analysis showed that the therapeutic effect of matrine against DLBCL may be related to the PI3K-Akt signaling pathway.

Conclusion: Matrine may target five genes and the PI3K-Akt signaling pathway in DLBCL treatment.

Introduction: Sepsis is a state of the systemic inflammatory response of the host induced by infection, frequently affecting numerous organs and producing varied degrees of damage. The most typical consequence of sepsis is sepsis-associated acute kidney injury(SA-AKI). Xuebijing is developed based on XueFuZhuYu Decoction. Five Chinese herbal extracts, including Carthami Flos, Radix Paeoniae Rubra, Chuanxiong Rhizoma, Radix Salviae, and Angelicae Sinensis Radix, make up the majority of the mixture. It has properties that are anti-inflammatory and anti-oxidative stress. Xuebijing is an effective medication for the treatment of SA-AKI, according to clinical research. But its pharmacological mechanism is still not completely understood.

Methods: First, the composition and target information of Carthami Flos, Radix Paeoniae Rubra, Chuanxiong Rhizoma, Radix Salviae, and Angelicae Sinensis Radix were collected from the TCMSP database, while the therapeutic targets of SA-AKI were exported from the gene card database. To do a GO and KEGG enrichment analysis, we first screened the key targets using a Venn diagram and Cytoscape 3.9.1. To assess the binding activity between the active component and the target, we lastly used molecular docking.

Results: For Xuebijing, a total of 59 active components and 267 corresponding targets were discovered, while for SA-AKI, a total of 1,276 targets were connected. There were 117 targets in all that was shared by goals for active ingredients and objectives for diseases. The TNF signaling pathway and the AGE-RAGE pathway were later found to be significant pathways for the therapeutic effects of Xuebijing by GO analysis and KEGG pathway analysis. Quercetin, luteolin, and kaempferol were shown to target and modulate CXCL8, CASP3, and TNF, respectively, according to molecular docking results.

Conclusion: This study predicts the mechanism of action of the active ingredients of Xuebijing in the treatment of SA-AKI, which provides a basis for future applications of Xuebijing and studies targeting the mechanism.

Background: Leptin is predominant in regulating body weight by stimulating energy expenditure through its neuronal action in the brain. Moreover, it is projected to adipose tissue and induces adipocyte browning by activating the β3-adrenergic receptor (β3AR). However, the expression of leptin receptor (Lep-R) and β3AR in people with obesity is downregulated.

Aim: We hypothesized that Hibiscus sabdariffa Linn. extract (HSE) would increase hypothalamus arcuate nucleus (ARC) Lep-R and white adipose tissue (WAT) β3AR mRNA expression in DIO rats. This study also analyzed the potency of H. sabdariffa bioactive compounds as activators of Lep-R and β3AR by an in-silico experiment.

Methods: Twenty-four male Sprague-Dawley rats were divided into four groups: Control (standard food), DIO (high-fat diet), DIO-Hib200 (HFD+HSE 200 mg/kg BW), and DIO-Hib400 (HFD+HSE400 mg/kg BW). HSE was administered orally for five weeks, once a day.

Results: HSE administration significantly (p <0,05) increased the ARC Lep-R expression. The Lee index significantly decreased to the normal range (≤ 310) with p <0,001 for DIO-Hib200 and p <0,01 for DIO-Hib400. Among 39 bioactive compounds, 5-O-caffeoyl shikimic acid exhibited high free binding scores (-8,63) for Lep-R, and myricetin_3_arabinogalactoside had high free binding scores (-9,39) for β3AR. These binding predictions could activate Lep-R and β3AR.

Conclusion: This study highlights that HSE could be a potential therapeutic target for obesity by increasing LepR mRNA and leptin sensitivity, enhancing energy expenditure, and reducing obesity.