Current computer-aided drug design最新文献

Introduction: To investigate the mechanism of Danggui Liuhuang Tang (DGLHT) in the treatment of hyperthyroidism (HT), we explored the multi-component, multi-target, and multi-pathway mechanism based on the network pharmacology method of traditional Chinese medicine.

Objective: Using network pharmacology and molecular docking, the effective components, core targets, and critical pathways of DGLHT in the therapy of HT were investigated. The mechanism of DGLHT in the treatment of HT is discussed in this work, which also offers a scientific foundation for further research into the process.

Methods: To take DGLHT into the blood components as the research object, we used GeneCards, Drungbank, Therapeutic Target Database (TTD), Online Mendelian Inheritance in Man (OMIM), Pharmacogenetics and Pharmacogenomics Knowledge Base (PharmGKB), and other databases to predict the potential target of the components. Then, it was integrated with the predicted targets of HT disease to obtain the potential targets of DGLHT in the treatment of HT. We used String database and Cytoscape software for protein-protein interaction network (PPI) construction, and DAVID platform for Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway annotation, the Cytoscape software was used to construct a "component-target-pathway" network; the AutoDock Vina platform was used to conduct molecular docking between the blood entry components and key targets.

Results: According to the analysis, a total of 93 active ingredients, 348 disease-related targets, and 36 potential targets were screened out. Among them, key targets such as MAPK1, CCND1, AKT1, and TNF exert curative effects, and the main pathways are the HIF-1 signaling pathway, FoxO signaling pathway, Chemokine signaling pathway, TNF signaling pathway, Toll-like receptor signaling pathway, T cell receptor signaling pathway, Jak-STAT signaling pathway, and other pathways. Molecular docking results verified the interaction between active ingredients and key targets, among which rustication and quercetin had high docking affinity with key target proteins MAPK1 and CCND1.

Conclusion: This study preliminary revealed that DGLHT has the characteristics of multi-component, multi-target, and multi-pathway in the treatment of HT, and it established a scientific foundation for a more detailed investigation of DGLHT's molecular mechanism in the treatment of HT.

Background: To our knowledge, there is still a lack of scientific reports on the pharmacological mechanism of the Zuogui Pill (ZGP) for treating osteoporosis (OP).

Aims: This study aimed to explore it via network pharmacology and molecular docking.

Methods: We identified active compounds and associated targets in ZGP via two drug databases. Disease targets of OP were obtained utilizing five disease databases. Networks were established and analyzed through the Cytoscape software and STRING databases. Enrichment analyses were performed using the DAVID online tools. Molecular docking was performed using Maestro, PyMOL, and Discovery Studio software.

Results: 89 drug active compounds, 365 drug targets, 2514 disease targets, and 163 drug-disease common targets were obtained. Quercetin, kaempferol, phenylalanine, isorhamnetin, betavulgarin, and glycitein may be the crucial compounds of ZGP in treating OP. AKT1, MAPK14, RELA, TNF, and JUN may be the most important therapeutic targets. Osteoclast differentiation, TNF, MAPK, and thyroid hormone signaling pathways may be the critical therapeutic signaling pathways. The potential therapeutic mechanism mainly relates to osteoblastic or osteoclastic differentiation, oxidative stress, and osteoclastic apoptosis.

Conclusion: This study has revealed the anti-OP mechanism of ZGP, which offers objective evidence for relevant clinical application and further basic research.

Background: Aromatase inhibitors are used in the treatment of breast cancer as they are effective in decreasing the concentration of estrogen. As SNPs impact the efficacy or toxicity of drugs, evaluating them with mutated conformations would help in identifying potential inhibitors. In recent years, phytocompounds have been under scrutiny for their activity as potential inhibitors.

Objective: In this study, we have evaluated Centella asiatica compounds for their activity on aromatase with clinically significant SNPs: rs700519, rs78310315 and rs56658716.

Methods: Using AMDock v.1.5.2, which uses the AutoDock Vina engine, molecular docking simulations were carried out, and the docked complexes were analyzed for their chemical interactions such as polar contacts using PyMol v2.5. The mutated conformations of the protein and force field energy differences were computationally derived using SwissPDB Viewer. PubChem, dbSNP and ClinVar databases were used to retrieve the compounds and SNPs. ADMET prediction profile was generated using admetSAR v1.0.

Results: Docking simulations of the C. asiatica compounds with the native and mutated conformations showed that out of the obtained fourteen phytocompounds, Isoquercetin, Quercetin and 9H-Fluorene-2-carboxylic acid were able to dock with best scores in terms of binding affinity (- 8.4kcal/mol), Estimated Ki (0.6 μM) values and Polar Contacts in both native and mutated conformations (3EQM, 5JKW, 3S7S).

Conclusion: Our computational analyses predict that the deleterious SNPs did not impact the molecular interactions of Isoquercetin, Quercetin and 9H-Fluorene-2-carboxylic acid, providing better lead compounds for further evaluation as potential aromatase inhibitors.

Introduction: This study aimed to determine the in vitro and in silico effects of some natural and synthetic molecules on acetylcholinesterase (AChE), butyrylcholinesterase (BChE) and α-glucosidase enzymes.

Background: Alzheimer's disease (AD) and Type II diabetes mellitus (T2DM) are considered the most important diseases of today's world. However, the side effects of therapeutic agents used in both diseases limit their use. Therefore, developing drugs with high therapeutic efficacy and better pharmacological profile is important.

Objectives: This study sets out to determine the related enzyme inhibitors used in treating AD and T2DM, considered amongst the most important diseases of today's world.

Methods: In the current study, the in vitro and in silico effects of dienestrol, hesperetin, Lthyroxine, 3,3',5-Triiodo-L-thyronine (T3) and dobutamine molecules on AChE, BChE and α - glycosidase enzyme activities were investigated.

Results: All the molecules showed an inhibitory effect on the enzymes. The IC₅₀ and K_i values of the L-Thyroxine molecule, which showed the strongest inhibition effect for the AChE enzyme, were determined as 1.71 μM and 0.83 ± 0.195 μM, respectively. In addition, dienestrol, T3, and dobutamine molecules showed a more substantial inhibition effect than tacrine. The dobutamine molecule showed the most substantial inhibition effect for the BChE enzyme, and IC₅₀ and K_i values were determined as 1.83 μM and 0.845 ± 0.143 μM, respectively. The IC₅₀ and K_i values for the hesperetin molecule, which showed the strongest inhibition for the α -glycosidase enzyme, were determined as 13.57 μM and 12.33 ± 2.57 μM, respectively.

Conclusion: According to the results obtained, the molecules used in the study may be considered potential inhibitor candidates for AChE, BChE and α-glycosidase.

Aims: In this work, some new chromeno[4',3'-b]pyrano[6,5-d]pyrimidines,3-amino and 3-methyl-5-aryl-4-imino-5(H)-chromeno[4',3'-b]pyrano[6,5-d]pyrimidine-6-ones derivatives were synthesized.

Background: Chromenopyrimidines have attracted significant attention recently because of their activities, such as antiviral and cytotoxic activity.

Objective: All synthesized compounds were characterized using IR, ¹H-NMR, Mass Spectroscopy, and elemental analysis data.

Methods: Molecular docking studies were carried out to determine the inhibitory action of studied ligands against the Main Protease (6LU7, 6m03) of coronavirus (COVID-19). Moreover, the Lipinski Rule parameters were calculated for the synthesized compounds.

Results: The result of the docking studies showed a significant inhibitory action against the Main protease (M^pro) of SARS-CoV-2, and the binding energy (ΔG) values of the ligands against the protein (6LU7, 6M03) are -7.8 to -9.9 Kcal/mole.

Conclusion: It may conclude that some ligands were likely to be considered lead-like against the main protease of SARS-CoV-2.

Aims: This study aims to evaluate the efficacy and safety of PARP inhibitor therapy in advanced ovarian cancer and identify the optimal treatment for the survival of patients.

Background: The diversity of PARP inhibitors makes clinicians confused about the optimal strategy and the most effective BRCAm mutation-based regimen for the survival of patients with advanced ovarian cancer.

Objectives: The objective of this study is to compare the effects of various PARP inhibitors alone or in combination with other agents in advanced ovarian cancer.

Methods: PubMed, Embase, Cochrane Library, and Web of Science were searched for relevant studies on PARP inhibitors for ovarian cancer. Bayesian network meta-analysis was performed using Stata 15.0 and R 4.0.4. The primary outcome was the overall PFS, and the secondary outcomes included OS, AE3, DISAE, and TFST.

Results: Fifteen studies involving 5,788 participants were included. The Bayesian network metaanalysis results showed that olaparibANDAI was the most beneficial in prolonging overall PFS and non-BRCAm PFS, followed by niraparibANDAI. However, for BRCAm patients, olaparibTR might be the most effective, followed by niraparibANDAI. Olaparib was the most effective for the OS of BRCAm patients. AI, olaparibANDAI, and veliparibTR were more likely to induce grade 3 or higher adverse events. AI and olaparibANDAI were more likely to cause DISAE.

Conclusion: PARP inhibitors are beneficial to the survival of patients with advanced ovarian cancer. The olaparibTR is the most effective for BRCAm patients, whereas olaparibANDAI and niraparibANDAI are preferable for non-BRCAm patients. Other: More high-quality studies are desired to investigate the efficacy and safety of PARP inhibitors in patients with other genetic performances.

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: To decipher the underlying mechanisms of Sanleng-Ezhu for the treatment of idiopathic pulmonary fibrosis based on network pharmacology and single-cell RNA sequencing data.

Background: Idiopathic Pulmonary Fibrosis (IPF) is the most common type of interstitial lung disease. Although the combination of herbs Sanleng (SL) and Ezhu (EZ) has shown reliable efficacy in the management of IPF, its underlying mechanisms remain unknown.

Methods: Based on LC-MS/MS analysis and the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database, we identified the bioactive components of SL-EZ. After obtaining the IPF-related dataset GSE53845 from the Gene Expression Omnibus (GEO) database, we performed the differential expression analysis and the weighted gene co-expression network analysis (WGCNA), respectively. We obtained lowly and highly expressed IPF subtype gene sets by comparing Differentially Expressed Genes (DEGs) with the most significantly negatively and positively related IPF modules in WGCNA. Subsequently, we performed Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on IPF subtype gene sets. The low- and highexpression MCODE subgroup feature genes were identified by the MCODE plug-in and were adopted for Disease Ontology (DO), GO, and KEGG enrichment analyses. Next, we performed the immune cell infiltration analysis of the MCODE subgroup feature genes. Single-cell RNA sequencing analysis demonstrated the cell types which expressed different MCODE subgroup feature genes. Molecular docking and animal experiments validated the effectiveness of SL-EZ in delaying the progression of pulmonary fibrosis.

Results: We obtained 5 bioactive components of SL-EZ as well as their corresponding 66 candidate targets. After normalizing the samples of the GSE53845 dataset from the GEO database source, we obtained 1907 DEGs of IPF. Next, we performed a WGCNA analysis on the dataset and got 11 modules. Notably, we obtained 2 IPF subgroups by contrasting the most significantly up- and down-regulated modular genes in IPF with DEGs, respectively. The different IPF subgroups were compared with drugcandidate targets to obtain direct targets of action. After constructing the protein interaction networks between IPF subgroup genes and drug candidate targets, we applied the MCODE plug-in to filter the highest-scoring MCODE components. DO, GO, and KEGG enrichment analyses were applied to drug targets, IPF subgroup genes, and MCODE component signature genes. In addition, we downloaded the single-cell dataset GSE157376 from the GEO database. By performing quality control and dimensionality reduction, we clustered the scattered primary sample cells into 11 clusters and annotated them into 2 cell subtypes. Drug sensitivity analysis suggested that SL-EZ acts on different cell subtypes in IPF subgroups. Molecula

Background: Traditional Chinese medicine (TCM) Xiao Jianzhong Tang (XJZ) has a favorable efficacy in the treatment of chronic atrophic gastritis (CAG). However, its pharmacological mechanism has not been fully explained.

Objective: The purpose of this study was to find the potential mechanism of XJZ in the treatment of CAG using pharmacocoinformatics approaches.

Methods: Network pharmacology was used to screen out the key compounds and key targets, MODELLER and GNNRefine were used to repair and refine proteins, Autodock vina was employed to perform molecular docking, Δ _{Lin_F9}XGB was used to score the docking results, and Gromacs was used to perform molecular dynamics simulations (MD).

Results: Kaempferol, licochalcone A, and naringenin, were obtained as key compounds, while AKT1, MAPK1, MAPK14, RELA, STAT1, and STAT3 were acquired as key targets. Among docking results, 12 complexes scored greater than five. They were run for 50ns MD. The free binding energy of AKT1-licochalcone A and MAPK1-licochalcone A was less than -15 kcal/mol and AKT1-naringenin and STAT3-licochalcone A was less than -9 kcal/mol. These complexes were crucial in XJZ treating CAG.

Conclusion: Our findings suggest that licochalcone A could act on AKT1, MAPK1, and STAT3, and naringenin could act on AKT1 to play the potential therapeutic effect on CAG. The work also provides a powerful approach to interpreting the complex mechanism of TCM through the amalgamation of network pharmacology, deep learning-based protein refinement, molecular docking, machine learning-based binding affinity estimation, MD simulations, and MM-PBSA-based estimation of binding free energy.

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.