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Showing 741 to 744 of 5186 entries
Published on July 11, 2022
Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.
Abstract: CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique challenges leading to extremely high attrition rates and low efficiency. With explosion of data in information age, there is hardly any aspect of life that has not been touched by data driven technologies such as artificial intelligence (AI) and machine learning (ML). Drug discovery is no exception, emergence of big data via genomic, proteomic, biological, and chemical technologies has driven pharmaceutical giants to collaborate with AI oriented companies to revolutionise drug discovery, with the goal of increasing the efficiency of the process. In recent years many examples of innovative applications of AI and ML techniques in CNS drug discovery has been reported. Research on therapeutics for diseases such as schizophrenia, Alzheimer's and Parkinsonism has been provided with a new direction and thrust from these developments. AI and ML has been applied to both ligand-based and structure-based drug discovery and design of CNS therapeutics. In this review, we have summarised the general aspects of AI and ML from the perspective of drug discovery followed by a comprehensive coverage of the recent developments in the applications of AI/ML techniques in CNS drug discovery.
Published on July 11, 2022
Qin Huang formula enhances the effect of Adriamycin in B-cell lymphoma via increasing tumor infiltrating lymphocytes by targeting toll-like receptor signaling pathway.
Abstract: BACKGROUND: As an original traditional Chinese medicinal formula, Qin Huang formula (QHF) is used as adjuvant therapy for treating lymphoma in our hospital and has proven efficacy when combined with chemotherapy. However, the underlying mechanisms of QHF have not been elucidated. METHODS: A network pharmacological-based analysis method was used to screen the active components and predict the potential mechanisms of QHF in treating B cell lymphoma. Then, a murine model was built to verify the antitumor effect of QHF combined with Adriamycin (ADM) in vivo. Finally, IHC, ELISA, (18)F-FDG PET-CT scan, and western blot were processed to reveal the intriguing mechanism of QHF in treating B cell lymphoma. RESULTS: The systemic pharmacological study revealed that QHF took effect following a multiple-target and multiple-pathway pattern in the human body. In vivo study showed that combination therapy with QHF and ADM potently inhibited the growth of B cell lymphoma in a syngeneic murine model, and significantly increased the proportion of tumor infiltrating CD4+ and CD8+ T cells in the tumor microenvironment (TME). Furthermore, the level of CXCL10 and IL-6 was significantly increased in the combination group. Finally, the western blot exhibited that the level of TLR2 and p38 MAPK increased in the combination therapy group. CONCLUSION: QHF in combination of ADM enhances the antitumor effect of ADM via modulating tumor immune microenvironment and can be a combination therapeutic strategy for B cell lymphoma patients.
Published on July 11, 2022
Machine learning-based quantitative prediction of drug exposure in drug-drug interactions using drug label information.
Abstract: Many machine learning techniques provide a simple prediction for drug-drug interactions (DDIs). However, a systematically constructed database with pharmacokinetic (PK) DDI information does not exist, nor is there a machine learning model that numerically predicts PK fold change (FC) with it. Therefore, we propose a PK DDI prediction (PK-DDIP) model for quantitative DDI prediction with high accuracy, while constructing a highly reliable PK-DDI database. Reliable information of 3,627 PK DDIs was constructed from 3,587 drugs using 38,711 Food and Drug Administration (FDA) drug labels. This PK-DDIP model predicted the FC of the area under the time-concentration curve (AUC) within +/- 0.5959. The prediction proportions within 0.8-1.25-fold, 0.67-1.5-fold, and 0.5-2-fold of the AUC were 75.77, 86.68, and 94.76%, respectively. Two external validations confirmed good prediction performance for newly updated FDA labels and FC from patients'. This model enables potential DDI evaluation before clinical trials, which will save time and cost.
Published on July 11, 2022
The heterogeneous pharmacological medical biochemical network PharMeBINet.
Abstract: Heterogeneous biomedical pharmacological databases are important for multiple fields in bioinformatics. Hetionet is a freely available database combining diverse entities and relationships from 29 public resources. Therefore, it is used as the basis for this project. 19 additional pharmacological medical and biological databases such as CTD, DrugBank, and ClinVar are parsed and integrated into Neo4j. Afterwards, the information is merged into the Hetionet structure. Different mapping methods are used such as external identification systems or name mapping. The resulting open-source Neo4j database PharMeBINet has 2,869,407 different nodes with 66 labels and 15,883,653 relationships with 208 edge types. It is a heterogeneous database containing interconnected information on ADRs, diseases, drugs, genes, gene variations, proteins, and more. Relationships between these entities represent drug-drug interactions or drug-causes-ADR relations, to name a few. It has much potential for developing further data analyses including machine learning applications. A web application for accessing the database is free to use for everyone and available at https://pharmebi.net . Additionally, the database is deposited on Zenodo at https://doi.org/10.5281/zenodo.6578218 .
Published on July 8, 2022
Exploring the molecular interaction of mebendazole with bovine serum albumin using multi-spectroscopic approaches and molecular docking.
Abstract: This article presents the binding interaction between mebendazole (MBZ) and bovine serum albumin. The interaction has been studied using different techniques, such as fluorescence quenching spectroscopy, UV-visible spectroscopy, synchronous fluorescence spectroscopy, fourier transform infrared, and fluorescence resonance energy transfer in addition to molecular docking. Results from Stern Volmer equation stated that the quenching for MBZ-BSA binding was static. The fluorescence quenching spectroscopic study was performed at three temperature settings. The binding constant (kq), the number of binding sites (n), thermodynamic parameters (DeltaH(omicron), DeltaS(omicron) and DeltaG(omicron)), and binding forces were determined. The results exhibited that the interaction was endothermic. It was revealed that intermolecular hydrophobic forces led to the stabilization of the drug-protein system. Using the site marker technique, the binding between MBZ and BSA was found to be located at subdomain IIA (site I). This was furtherly approved using the molecular docking technique with the most stable MBZ configuration. This research may aid in understanding the pharmacokinetics and toxicity of MBZ and give fundamental data for its safe usage to avoid its toxicity.
Published on July 8, 2022
Identification of molecular mechanisms underlying the therapeutic effects of Xintong granule in coronary artery disease by a network pharmacology and molecular docking approach.
Abstract: Coronary artery disease (CAD) is a cardiovascular disease characterized by atherosclerosis, angiogenesis, thrombogenesis, inflammation, etc. Xintong granule (XTG) is considered a practical therapeutic strategy in China for CAD. Although its therapeutic role in CAD has been reported, the molecular mechanisms of XTG in CAD have not yet been explored. A network pharmacology approach including drug-likeness (DL) evaluation, oral bioavailability (OB) prediction, protein-protein interaction (PPI) network construction and analysis, and Gene Ontology term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses was used to predict the active ingredients, potential targets, and molecular mechanisms of XTG associated with the treatment of CAD. Molecular docking analysis was performed to investigate the interactions between the active compounds and the underlying targets. Fifty-one active ingredients of XTG and 294 CAD-related targets were screened for analysis. Gene Ontology enrichment analysis showed that the therapeutic targets of XTG in CAD are mainly involved in blood circulation and vascular regulation. KEGG pathway analysis indicated that XTG intervenes in CAD mainly through the regulation of fluid shear stress and atherosclerosis, the AGE-RAGE signaling pathway in diabetic complications, and the relaxin signaling pathway. Molecular docking analysis showed that each key active ingredient (quercetin, luteolin, kaempferol, stigmasterol, resveratrol, fisetin, gamma-sitosterol, and beta-sitosterol) of XTG can bind to the core targets of CAD (AKT1, JUN, RELA, MAPK8, NFKB1, EDN1, and NOS3). The present study revealed the CAD treatment-related active ingredients, underlying targets, and potential molecular mechanisms of XTG acting by regulating fluid shear stress and atherosclerosis, AGE-RAGE signaling pathway in diabetic complications, and relaxin signaling pathway.
Published on July 8, 2022
In-Silico Screening and Molecular Dynamics Simulation of Drug Bank Experimental Compounds against SARS-CoV-2.
Abstract: For the last few years, the world has been going through a difficult time, and the reason behind this is severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2), one of the significant members of the Coronaviridae family. The major research groups have shifted their focus towards finding a vaccine and drugs against SARS-CoV-2 to reduce the infection rate and save the life of human beings. Even the WHO has permitted using certain vaccines for an emergency attempt to cut the infection curve down. However, the virus has a great sense of mutation, and the vaccine's effectiveness remains questionable. No natural medicine is available at the community level to cure the patients for now. In this study, we have screened the vast library of experimental drugs of Drug Bank with Schrodinger's maestro by using three algorithms: high-throughput virtual screening (HTVS), standard precision, and extra precise docking followed by Molecular Mechanics/Generalized Born Surface Area (MMGBSA). We have identified 3-(7-diaminomethyl-naphthalen-2-YL)-propionic acid ethyl ester and Thymidine-5'-thiophosphate as potent inhibitors against the SARS-CoV-2, and both drugs performed impeccably and showed stability during the 100 ns molecular dynamics simulation. Both of the drugs are among the category of small molecules and have an acceptable range of ADME properties. They can be used after their validation in in-vitro and in-vivo conditions.
Published on July 8, 2022
COVIDPUBGRAPH: A FAIR Knowledge Graph of COVID-19 Publications.
Abstract: The rapid generation of large amounts of information about the coronavirus SARS-CoV-2 and the disease COVID-19 makes it increasingly difficult to gain a comprehensive overview of current insights related to the disease. With this work, we aim to support the rapid access to a comprehensive data source on COVID-19 targeted especially at researchers. Our knowledge graph, COVIDPUBGRAPH, an RDF knowledge graph of scientific publications, abides by the Linked Data and FAIR principles. The base dataset for the extraction is CORD-19, a dataset of COVID-19-related publications, which is updated regularly. Consequently, COVIDPUBGRAPH is updated biweekly. Our generation pipeline applies named entity recognition, entity linking and link discovery approaches to the original data. The current version of COVIDPUBGRAPH contains 268,108,670 triples and is linked to 9 other datasets by over 1 million links. In our use case studies, we demonstrate the usefulness of our knowledge graph for different applications. COVIDPUBGRAPH is publicly available under the Creative Commons Attribution 4.0 International license.
Published on July 7, 2022
BitterMatch: recommendation systems for matching molecules with bitter taste receptors.
Abstract: Bitterness is an aversive cue elicited by thousands of chemically diverse compounds. Bitter taste may prevent consumption of foods and jeopardize drug compliance. The G protein-coupled receptors for bitter taste, TAS2Rs, have species-dependent number of subtypes and varying expression levels in extraoral tissues. Molecular recognition by TAS2R subtypes is physiologically important, and presents a challenging case study for ligand-receptor matchmaking. Inspired by hybrid recommendation systems, we developed a new set of similarity features, and created the BitterMatch algorithm that predicts associations of ligands to receptors with ~ 80% precision at ~ 50% recall. Associations for several compounds were tested in-vitro, resulting in 80% precision and 42% recall. The encouraging performance was achieved by including receptor properties and integrating experimentally determined ligand-receptor associations with chemical ligand-to-ligand similarities.BitterMatch can predict off-targets for bitter drugs, identify novel ligands and guide flavor design. The novel features capture information regarding the molecules and their receptors, which could inform various chemoinformatic tasks. Inclusion of neighbor-informed similarities improves as experimental data mounts, and provides a generalizable framework for molecule-biotarget matching.
Published on July 6, 2022
Drug-Target Network Study Reveals the Core Target-Protein Interactions of Various COVID-19 Treatments.
Abstract: The coronavirus disease 2019 (COVID-19) pandemic has caused a dramatic loss of human life and devastated the worldwide economy. Numerous efforts have been made to mitigate COVID-19 symptoms and reduce the death rate. We conducted literature mining of more than 250 thousand published works and curated the 174 most widely used COVID-19 medications. Overlaid with the human protein-protein interaction (PPI) network, we used Steiner tree analysis to extract a core subnetwork that grew from the pharmacological targets of ten credible drugs ascertained by the CTD database. The resultant core subnetwork consisted of 34 interconnected genes, which were associated with 36 drugs. Immune cell membrane receptors, the downstream cellular signaling cascade, and severe COVID-19 symptom risk were significantly enriched for the core subnetwork genes. The lung mast cell was most enriched for the target genes among 1355 human tissue-cell types. Human bronchoalveolar lavage fluid COVID-19 single-cell RNA-Seq data highlighted the fact that T cells and macrophages have the most overlapping genes from the core subnetwork. Overall, we constructed an actionable human target-protein module that mainly involved anti-inflammatory/antiviral entry functions and highly overlapped with COVID-19-severity-related genes. Our findings could serve as a knowledge base for guiding drug discovery or drug repurposing to confront the fast-evolving SARS-CoV-2 virus and other severe infectious diseases.
Showing 741 to 744 of 5186 entries