PubChem (https://pubchem.ncbi.nlm.nih.gov) is a large and highly-integrated public chemical database resource at NIH. In the past two years, significant updates were made to PubChem. With additions from over 130 new sources, PubChem contains >1000 data sources, 119 million compounds, 322 million substances and 295 million bioactivities. New interfaces, such as the consolidated literature panel and the patent knowledge panel, were developed. The consolidated literature panel combines all references about a compound into a single list, allowing users to easily find, sort, and export all relevant articles for a chemical in one place. The patent knowledge panels for a given query chemical or gene display chemicals, genes, and diseases co-mentioned with the query in patent documents, helping users to explore relationships between co-occurring entities within patent documents. PubChemRDF was expanded to include the co-occurrence data underlying the literature knowledge panel, enabling users to exploit semantic web technologies to explore entity relationships based on the co-occurrences in the scientific literature. The usability and accessibility of information on chemicals with non-discrete structures (e.g. biologics, minerals, polymers, UVCBs and glycans) were greatly improved with dedicated web pages that provide a comprehensive view of all available information in PubChem for these chemicals.

PubChem 2025 update.

Drug repositioning is a faster and more affordable solution than traditional drug discovery approaches. From this perspective, computational drug repositioning using knowledge graphs is a very promising direction. Knowledge graphs constructed from drug data and information can be used to generate hypotheses (molecule/drug - target links) through link prediction using machine learning algorithms. However, it remains rare to have a holistically constructed knowledge graph using the broadest possible features and drug characteristics, which is freely available to the community. The OREGANO knowledge graph aims at filling this gap. The purpose of this paper is to present the OREGANO knowledge graph, which includes natural compounds related data. The graph was developed from scratch by retrieving data directly from the knowledge sources to be integrated. We therefore designed the expected graph model and proposed a method for merging nodes between the different knowledge sources, and finally, the data were cleaned. The knowledge graph, as well as the source codes for the ETL process, are openly available on the GitHub of the OREGANO project ( https://gitub.u-bordeaux.fr/erias/oregano ). 

The OREGANO knowledge graph for computational drug repurposing

Licorice (Glycyrrhiza) is a medicinal and food homologue of perennial plants derived from the dried roots and rhizomes of the genus Glycyrrhiza in the legume family. In recent years, the comprehensive utilization of licorice resources has attracted people's attention. It is widely utilized to treat diseases, health food products, food production, and other industrial applications. Furthermore, numerous bioactive components of licorice are found using advanced extraction processes, which mainly include polyphenols (flavonoids, dihydrostilbenes, benzofurans, and coumarin), triterpenoids, polysaccharides, alkaloids, and volatile oils, all of which have been reported to possess a variety of pharmacological characteristics, including anti-oxidant, anti-inflammatory, antibacterial, antiviral, anticancer, neuroprotective, antidepressive, antidiabetic, antiparasitic, antisex hormone, skin effects, anticariogenic, antitussive, and expectorant activities. Thereby, all of these compounds promote the development of novel and more effective licorice-derived products. This paper reviews the progress of research on extraction techniques, chemical composition, bioactivities, and applications of licorice to provide a reference for further development and application of licorice in different areas.

A Comprehensive Review of Licorice: the Preparation, Chemical Composition, Bioactivities and Its Applications.

Knowledge of the collective activities of individual plants together with the derived clinical effects and targeted disease associations is useful for plant-based biomedical research. To provide the information in complement to the established databases, we introduced a major update of CMAUP database, previously featured in NAR. This update includes (i) human transcriptomic changes overlapping with 1152 targets of 5765 individual plants, covering 74 diseases from 20 027 patient samples; (ii) clinical information for 185 individual plants in 691 clinical trials; (iii) drug development information for 4694 drug-producing plants with metabolites developed into approved or clinical trial drugs; (iv) plant and human disease associations (428 737 associations by target, 220 935 reversion of transcriptomic changes, 764 and 154121 associations by clinical trials of individual plants and plant ingredients); (v) the location of individual plants in the phylogenetic tree for navigating taxonomic neighbors, (vi) DNA barcodes of 3949 plants, (vii) predicted human oral bioavailability of plant ingredients by the established SwissADME and HobPre algorithm, (viii) 21-107% increase of CMAUP data over the previous version to cover 60 222 chemical ingredients, 7865 plants, 758 targets, 1399 diseases, 238 KEGG human pathways, 3013 gene ontologies and 1203 disease ontologies. CMAUP update version is freely accessible at https://bidd.group/CMAUP/index.html.

CMAUP database update 2024: extended functional and association information of useful plants for biomedical research.

Polymethoxylated flavonoids (PMFs) are a subclass of flavonoids found in citrus fruits that have shown multifunctional biological activities and potential anticancer effects against breast cancer. We studied the absorption, metabolism, species source, toxicity, anti-cancer mechanisms, and molecular targets of PMFs to better utilize their anticancer activity against breast cancer. We discuss the absorption and metabolism of PMFs in the body, including the methylation, demethylation, and hydroxylation processes. The anticancer mechanisms of PMFs against breast cancer were also reviewed, including the estrogen activity, cytochrome P-450 enzyme system, and arylhydrocarbon receptor (AhR) inhibition, along with various molecular targets and potential anticancer effects. Although PMFs may be advantageous in the prevention and treatment for breast cancer, there is a lack of clinical evidence and data to support their efficacy. Despite their promise, there is still a long way to go before PMFs can be applied clinically.

Polymethoxylated flavonoids in citrus fruits: absorption, metabolism, and anticancer mechanisms against breast cancer

Abstract Quantitative activity and species source data of natural products (NPs) are important for drug discovery, medicinal plant research, and microbial investigations. Activity values of NPs against specific targets are useful for discovering targeted therapeutic agents and investigating the mechanism of medicinal plants. Composition/concentration values of NPs in individual species facilitate the assessments and investigations of the therapeutic quality of herbs and phenotypes of microbes. Here, we describe an update of the NPASS natural product activity and species source database previously featured in NAR. This update includes: (i) new data of ∼95 000 records of the composition/concentration values of ∼1 490 NPs/NP clusters in ∼390 species, (ii) extended data of activity values of ∼43 200 NPs against ∼7 700 targets (∼40% and ∼32% increase, respectively), (iii) extended data of ∼31 600 species sources of ∼94 400 NPs (∼26% and ∼32% increase, respectively), (iv) new species types of ∼440 co-cultured microbes and ∼420 engineered microbes, (v) new data of ∼66 600 NPs without experimental activity values but with estimated activity profiles from the established chemical similarity tool Chemical Checker, (vi) new data of the computed drug-likeness properties and the absorption, distribution, metabolism, excretion and toxicity (ADMET) properties for all NPs. NPASS update version is freely accessible at http://bidd.group/NPASS.

NPASS database update 2023: quantitative natural product activity and species source database for biomedical research

Chemical Structure-based Graph Convolutional Model for Drug-Gut Microbiota Association Prediction

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NPASS database update 2023: quantitative natural product activity and species source database for biomedical research

CMAUP database update 2024: extended functional and association information of useful plants for biomedical research.

Chemical Structure-based Graph Convolutional Model for Drug-Gut Microbiota Association Prediction