PubChem Protein, Gene, Pathway, and Taxonomy Data Collections: Bridging Biology and Chemistry through Target-Centric Views of PubChem Data

Tolypothrix Strains (Cyanobacteria) as a Source of Bioactive Compounds with Anticancer, Antioxidant and Anti-Inflammatory Activity

Cyanobacterial extracts offer significant potential for the development of new natural antioxidants and biologically active compounds with applications in various industries. Data on the genus Tolypothrix are limited; therefore, the aim of the present study was to investigate the anticancer, antioxidant and anti-inflammatory activity of extracts prepared from strains of this genus. Cytotoxicity and anticancer activity were evaluated by in vitro tests with four cell lines using the MTT assay. The assessment of antioxidant activity was performed by the DPPH and ABTS methods in combination with the calculation of the total phenolic content. Anti-inflammatory activity was investigated using the LPS-stimulated macrophage model (RAW264.7) and subsequent measurement of the levels of secreted cytokines IL-6 and TNF-α. The lipid content and fatty acid composition of the non-polar extracts were determined by gas chromatography (GC). To elucidate the mechanism of cytotoxicity/anticancer action of the non-polar extracts, the effects of stearidonic acid, which was detected in four of the studied cyanobacterial strains, were additionally tested on the same cell lines. A molecular docking analysis was performed simulating the interaction between stearidonic acid and its target molecules and receptors (ALOX5, COX-2, NF-kB and PPAR-γ). In all cancer cell lines (but not in the normal one), dose-dependent cytotoxic effects were observed after exposure to different concentrations of non-polar Tolypothrix extracts. The most pronounced inhibitory effect was observed on the HT-29 cell line, with an IC50 value of 106.27 µg/mL. A dose-dependent antioxidant effect was established for all tested extracts, measured by both DPPH and ABTS methods. All non-polar extracts reduced the production of pro-inflammatory cytokines IL-6 and TNF-α in LPS-stimulated macrophages RAW264.7, and the effects were dose-dependent. Analysis of the fatty acid composition revealed 26 different fatty acids. Our conclusion is that the Tolypothrix strains exhibit anticancer, antioxidant, and anti-inflammatory activity and could be a promising source for the production of natural products.

Identification and validation of shared biomarkers and drug repurposing in psoriasis and Crohn's disease: integrating bioinformatics, machine learning, and experimental approaches

Background

Psoriasis and Crohn's disease (CD) are chronic inflammatory diseases that involve complex immune-mediated mechanisms. Despite clinical overlap and shared genetic predispositions, the molecular pathways connecting these diseases remain incompletely understood. The present study seeks to identify shared biomarkers and therapeutic targets for psoriasis and CD.

Methods

Differentially expressed genes (DEGs) were identified from publicly available transcriptomic datasets related to psoriasis and CD. Simultaneously, weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules associated with the clinical traits of psoriasis and CD. Subsequently, biomarkers were prioritized from shared key genes by integrating protein-protein interaction (PPI) networks with machine learning models. Gene Set Enrichment Analysis (GSEA), along with Gene Ontology (GO) and KEGG pathway analyses, were performed to determine the biological significance of the identified genes. Immune infiltration analysis underscored the involvement of hub genes in immune regulation, while single-cell transcriptomic analysis revealed the cellular localization of these hub genes. Additional targeted molecular biology experiments validated the shared biomarkers. DSigDB predictions were employed to identify potential therapeutic compounds. Molecular docking simulations were performed to assess the binding affinity of the drugs to key target proteins. Finally, additional in vitro experiments were conducted to validate the therapeutic effects of the identified compounds.

Results

The study identified KIF4A, DLGAP5, NCAPG, CCNB1, and CEP55 as key regulatory molecules and shared biomarkers for both diseases. GSEA and pathway analysis highlighted the importance of cell cycle regulation and immune response pathways in the comorbidities of psoriasis and CD. Immune infiltration analysis emphasized the role of hub genes in immune regulation. Furthermore, DSigDB predictions and molecular docking simulations indicated strong therapeutic potential for Etoposide, Lucanthone, and Piroxicam, with Etoposide showing the highest affinity for key targets. In cellular models, Etoposide demonstrated promising therapeutic effects by significantly downregulating the expression of psoriasis-related keratinocytes marker genes (KRT6, KRT16) and CD-related inflammatory cytokines (IL6, IL8, TNF-α), highlighting its potential in treating psoriasis and CD.

Discussion

This study integrates bioinformatics, machine learning, and molecular validation to identify the shared molecular mechanisms of psoriasis and CD, uncovering novel biomarkers and potential combined therapeutic candidates. These findings provide valuable insights into potential treatment strategies for these diseases.

Exploration of Novel Antimicrobial Agents against Foodborne Pathogens via a Deep Learning Approach

The emergence of antibiotic-resistant bacteria poses a severe threat to food safety and human health, necessitating an urgent search for novel antimicrobial agents that can be applied in the food industry. This study utilizes a deep learning approach to establish the optimal models for antibacterial activity against foodborne pathogens, particularly Escherichia coli and Staphylococcus aureus, as well as for predicting carcinogenicity. These optimal models are applied to screen natural products from the COCONUT database, resulting in the identification of 130 compounds with both antibacterial activity and noncarcinogenic properties. Two natural products, bis(hexamethylene)triamine and N-phenethylbiguanide, are selected for experimental validation of their antibacterial activity. The confirmation of antimicrobial properties validates the reliability of the models developed in this study. By providing an innovative approach for identifying antimicrobial agents for foodborne pathogens, this research offers new insights for discovering effective antimicrobials in an efficient manner.

Enhanced Intranasal Delivery of Atorvastatin via Superparamagnetic Iron-Oxide-Loaded Nanocarriers: Cytotoxicity and Inflammation Evaluation and In Vivo, In Silico, and Network Pharmacology Study for Targeting Glioblastoma Management

Objective: This study aims to develop an intranasal (IN) delivery system for glioblastoma multiforme (GBM) management using repurposed superparamagnetic iron-oxide (SPION) loaded with atorvastatin (ATO)-nanostructured lipid carrier (NLC). Methods: Emulsification and ultrasonication were used to formulate ATO-NLCs, and the best formula was loaded with SPION to make the final atorvastatin/superparamagnetic iron oxide-loaded nanostructured lipid carrier (ASN) formulation. Entrapment efficiency (EE%), particle size (PS), zeta potential (ZP), and drug release after 6 h (Q6h) were evaluated for NLCs. ASN was tested for cytotoxicity on T98G cancer cells, and the cell cycle was examined to determine cell death. Furthermore, the ability of the optimal formulation to suppress the levels of inflammatory biomarkers was investigated in Lipopolysaccharide (LPS)-induced inflammation. The brain-targeting behavior of IN-ASN was visualized in rabbits via confocal laser scanning microscopy (CLSM). Results: The optimum NLC exhibited a spherical shape, EE% of 84.0 ± 0.67%, PS of 282.50 ± 0.51 nm, ZP of -18.40 ± 0.15 mV, and Q6h of 89.23%. The cytotoxicity of ASN against cancer cells was 4.4-fold higher than ATO suspension, with a 1.3-fold increment in cell apoptosis. ASN showed significantly reduced pro-inflammatory biomarkers (IL-β, IL-6, TNF-α, TLR4, NF-қB), whereas CLSM revealed enhanced brain delivery with no observed histopathological nasal irritation. The in silico analysis demonstrated enhanced ATO-ADME (absorption, distribution, metabolism, and excretion) properties, while the network pharmacology study identified 10 target GBM genes, among which MAPK3 was the most prominent with a good binding score as elucidated by the simulated docking study. Conclusions: These findings may present ATO/SPION-NLCs as significant evidence for repurposing atorvastatin in the treatment of glioblastoma multiforme.

Application and development trends of network toxicology in the safety assessment of traditional Chinese medicine

ETHNOPHARMACOLOGICAL RELEVANCE

Integrating network toxicology into traditional Chinese medicine toxicology boosts the efficiency and comprehensiveness of safety evaluations for these medicines.

AIM OF THE STUDY

This paper aims to deepen the comprehension of network toxicology and facilitate its application in the safety evaluation of traditional Chinese medicines.

MATERIALS AND METHODS

Using "network pharmacology," "network toxicology," and "traditional Chinese medicine" as keywords, relevant literature was searched in databases such as Web of Science, PubMed, and CNKI. The cited literature spans from 1999 to 2024.

RESULTS

Network toxicology constructs a "toxicity-gene-target-drug" network model to characterize the toxicological features of the research subject. This paper reviews existing toxicity prediction tools and databases, explores methodologies for toxicity prediction, and comprehensively summarizes the applications of network toxicology in the prediction of harmful components, analysis of toxicity mechanisms, integration of interdisciplinary approaches, and combination with omics technologies. Additionally, it analyzes the current challenges and limitations of network toxicology.

CONCLUSIONS

Network toxicology has been extensively utilized in the safety research of traditional Chinese medicine, particularly in identifying toxic components and elucidating their mechanisms. However, there remains significant space for advancement. Future research could investigate integrating network toxicology with cutting-edge technologies like artificial intelligence and multi-omics approaches, thereby offering robust theoretical foundations for developing a comprehensive traditional Chinese medicine safety evaluation system.

Glycoscience data content in the NCBI Glycans and PubChem

Studying glycans and their functions in the body aids in the understanding of disease mechanisms and developing new treatments. This necessitates resources that provide comprehensive glycan data integrated with relevant information from other scientific fields such as genomics, genetics, proteomics, metabolomics, and chemistry. The present paper describes two resources at the U.S. National Center for Biotechnology Information (NCBI), the NCBI Glycans and PubChem, which provide glycan-related information useful for the glycoscience research community. The NCBI Glycans ( https://www.ncbi.nlm.nih.gov/glycans/ ) is a dedicated website for glycobiology data content at NCBI and provides quick access to glycan-related information scattered across multiple NCBI databases as well as other information resources external to NCBI. Importantly, the NCBI Glycans hosts the official web page for the symbol nomenclature for glycans (SNFG), which is the standard graphical representation of glycan structures recommended for scientific publication. On the other hand, PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a research-focused, large-scale public chemical database, containing a substantial number of glycan-containing records and is integrated with important glycoscience resources like GlyTouCan, GlyCosmos, and GlyGen. PubChem organizes glycan-related information within multiple data collections (i.e., Substance, Compound, Protein, Gene, Pathway, and Taxonomy) and provides various tools and services that allow users to access them both interactively through a web browser and programmatically through a REST-ful interface, including PUG-View. The NCBI Glycans and PubChem highlight glycan-related data and improve their accessibility, helping scientists exploit these data in their research.

PubChem 2025 update

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.

Versatile properties of Opuntia ficus-indica (L.) Mill. flowers: In vitro exploration of antioxidant, antimicrobial, and anticancer activities, network pharmacology analysis, and In-silico molecular docking simulation

Opuntia ficus-indica (L.) Mill. has been used in folk medicine against several diseases. The objectives of the present study were to investigate the chemical composition of the methanolic extract of O. ficus-indica (L.) Mill. flowers and their antioxidant, antimicrobial, and anticancer properties. Besides, network pharmacology and molecular docking were used to explore the potential antitumor effect of active metabolites of O. ficus-indica (L.) Mill. against breast and liver cancer. The results revealed many bioactive components known for their antimicrobial and anticancer properties. Furthermore, scavenging activity was obtained, which indicated strong antioxidant properties. The plant extract exhibited antimicrobial activities against Aspergillus brasiliensis (MIC of 0.625 mg/mL), Candida albicans, Saccharomyces cerevisiae, Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa at MICs of 1.25 mg/mL. The results revealed proapoptotic activities of the O. ficus-indica (L.) Mill. extract against MCF7, MDA-MB-231, and HepG2 cell lines, where it induced significant early apoptosis and cell cycle arrest at sub-G1 phases, besides increasing the expression levels of p53, cyclin D1, and caspase 3 (p <0.005). The network pharmacology and molecular docking analysis revealed that the anticancer components of O. ficus-indica (L.) Mill. flower extract targets the PI3K-Akt pathway. More investigations might be required to test the mechanistic pathways by which O. ficus-indica (L.) Mill. might exhibit its biological activities in vivo.

FoodAtlas: Automated knowledge extraction of food and chemicals from literature

Automated generation of knowledge graphs that accurately capture published information can help with knowledge organization and access, which have the potential to accelerate discovery and innovation. Here, we present an integrated pipeline to construct a large-scale knowledge graph using large language models in an active learning setting. We apply our pipeline to the association of raw food, ingredients, and chemicals, a domain that lacks such knowledge resources. By using an iterative active learning approach of 4120 manually curated premise-hypothesis pairs as training data for ten consecutive cycles, the entailment model extracted 230,848 food-chemical composition relationships from 155,260 scientific papers, with 106,082 (46.0 %) of them never been reported in any published database. To augment the knowledge incorporated in the knowledge graph, we further incorporated information from 5 external databases and ontology sources. We then applied a link prediction model to identify putative food-chemical relationships that were not part of the constructed knowledge graph. Validation of the 443 hypotheses generated by the link prediction model resulted in 355 new food-chemical relationships, while results show that the model score correlates well (R2 = 0.70) with the probability of a novel finding. This work demonstrates how automated learning from literature at scale can accelerate discovery and support practical applications through reproducible, evidence-based capture of latent interactions of diverse entities, such as food and chemicals.

Exploration of compatibility rules and discovery of active ingredients in TCM formulas by network pharmacology

Network pharmacology is an interdisciplinary field that utilizes computer science, technology, and biological networks to investigate the intricate interplay among compounds/ingredients, targets, and diseases. Within the realm of traditional Chinese medicine (TCM), network pharmacology serves as a scientific approach to elucidate the compatibility relationships and underlying mechanisms of action in TCM formulas. It facilitates the identification of potential active ingredients within these formulas, providing a comprehensive understanding of their holistic and systematic nature, which aligns with the holistic principles inherent in TCM theory. TCM formulas exhibit complexity due to their multi-component characteristic, involving diverse targets and pathways. Consequently, investigating their material basis and mechanisms becomes challenging. Network pharmacology has emerged as a valuable approach in TCM formula research, leveraging its holistic and systematic advantages. The manuscript aims to provide an overview of the application of network pharmacology in studying TCM formula compatibility rules and explore future research directions. Specifically, we focus on how network pharmacology aids in interpreting TCM pharmacological theories and understanding formula compositions. Additionally, we elucidate the process of utilizing network pharmacology to identify active ingredients within TCM formulas. These findings not only offer novel research models and perspectives for integrating network pharmacology with TCM theory but also present new methodologies for investigating TCM formula compatibility. All in all, network pharmacology has become an indispensable and crucial tool in advancing TCM formula research.

Current approaches and outstanding challenges of functional annotation of metabolites: a comprehensive review

Metabolite profiling is a powerful approach for the clinical diagnosis of complex diseases, ranging from cardiometabolic diseases, cancer, and cognitive disorders to respiratory pathologies and conditions that involve dysregulated metabolism. Because of the importance of systems-level interpretation, many methods have been developed to identify biologically significant pathways using metabolomics data. In this review, we first describe a complete metabolomics workflow (sample preparation, data acquisition, pre-processing, downstream analysis, etc.). We then comprehensively review 24 approaches capable of performing functional analysis, including those that combine metabolomics data with other types of data to investigate the disease-relevant changes at multiple omics layers. We discuss their availability, implementation, capability for pre-processing and quality control, supported omics types, embedded databases, pathway analysis methodologies, and integration techniques. We also provide a rating and evaluation of each software, focusing on their key technique, software accessibility, documentation, and user-friendliness. Following our guideline, life scientists can easily choose a suitable method depending on method rating, available data, input format, and method category. More importantly, we highlight outstanding challenges and potential solutions that need to be addressed by future research. To further assist users in executing the reviewed methods, we provide wrappers of the software packages at https://github.com/tinnlab/metabolite-pathway-review-docker.

Mechanisms of action of Sappan lignum for prostate cancer treatment: network pharmacology, molecular docking and experimental validation

BACKGROUND

Prostate cancer (PCa) is the most common non-cutaneous malignancy in men globally. Sappan lignum, which exists in the heartwood of Caesalpinia sappan L., has antitumor effects; however, its exact mechanism of action remains unclear. This study elucidated the underlying mechanisms of Sappan lignum in PCa through network pharmacology approaches and molecular docking techniques. Moreover, the therapeutic effects of Sappan lignum on PCa were verified through in vitro experiments.

METHODS

The constituent ingredients of Sappan lignum were retrieved from the HERB database. Active plant-derived compounds of Sappan lignum were screened based on gastrointestinal absorption and gastric drug properties. Disease targets for PCa were screened using unpaired and paired case datasets from the Gene Expression Omnibus. Intersection targets were used for gene ontology and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis. Core targets were identified through topological analysis parameters and their clinical relevance was validated through The Cancer Genome Atlas database. The affinity between the phytochemicals of Sappan lignum and core proteins was verified using the molecular docking technique. Validation experiments confirmed the significant potential of Sappan lignum in treating PCa.

RESULTS

Twenty-one plant-derived compounds of Sappan lignum and 821 differentially expressed genes associated with PCa were collected. Among 32 intersection targets, 8 were screened according to topological parameters. KEGG analysis indicated that the antitumor effects of Sappan lignum on PCa were primarily associated with the p53 pathway. The molecular docking technique demonstrated a strong affinity between 3-deoxysappanchalcone (3-DSC) and core proteins, particularly cyclin B1 (CCNB1). CCNB1 expression correlated with clinicopathological features in patients with PCa. Experimental results revealed that 3-DSC exhibited anti-proliferative, anti-migratory, and pro-apoptotic effects on 22RV1 and DU145 cells while also causing G2/M phase cell cycle arrest, potentially through modulating the p53/p21/CDC2/CCNB1 pathway.

CONCLUSION

This research highlights the promising therapeutic potential of Sappan lignum in treating PCa, with a particular focus on targeting the p53 pathway.

Discovery of antiplasmodial pyridine carboxamides and thiocarboxamides

Malaria continues to be a significant burden, particularly in Africa, which accounts for 95% of malaria deaths worldwide. Despite advances in malaria treatments, malaria eradication is hampered by insecticide and antimalarial drug resistance. Consequently, the need to discover new antimalarial lead compounds remains urgent. To help address this need, we evaluated the antiplasmodial activity of twenty-two amides and thioamides with pyridine cores and their non-pyridine analogues. Twelve of these compounds showed in vitro anti-proliferative activity against the intraerythrocytic stage of Plasmodium falciparum, the most virulent species of Plasmodium infecting humans. Thiopicolinamide 13i was found to possess submicromolar activity (IC50 = 142 nM) and was >88-fold less active against a human cell line. The compound was equally effective against chloroquine-sensitive and -resistant parasites and did not inhibit β-hematin formation, pH regulation or PfATP4. Compound 13i may therefore possess a novel mechanism of action.

Deep learning algorithms applied to computational chemistry

Recently, there has been a significant increase in the use of deep learning techniques in the molecular sciences, which have shown high performance on datasets and the ability to generalize across data. However, no model has achieved perfect performance in solving all problems, and the pros and cons of each approach remain unclear to those new to the field. Therefore, this paper aims to review deep learning algorithms that have been applied to solve molecular challenges in computational chemistry. We proposed a comprehensive categorization that encompasses two primary approaches; conventional deep learning and geometric deep learning models. This classification takes into account the distinct techniques employed by the algorithms within each approach. We present an up-to-date analysis of these algorithms, emphasizing their key features and open issues. This includes details of input descriptors, datasets used, open-source code availability, task solutions, and actual research applications, focusing on general applications rather than specific ones such as drug discovery. Furthermore, our report discusses trends and future directions in molecular algorithm design, including the input descriptors used for each deep learning model, GPU usage, training and forward processing time, model parameters, the most commonly used datasets, libraries, and optimization schemes. This information aids in identifying the most suitable algorithms for a given task. It also serves as a reference for the datasets and input data frequently used for each algorithm technique. In addition, it provides insights into the benefits and open issues of each technique, and supports the development of novel computational chemistry systems.

Combining In Vitro, In Vivo, and Network Pharmacology Assays to Identify Targets and Molecular Mechanisms of Spirulina-Derived Biomolecules against Breast Cancer

The current research employed an animal model of 7,12-dimethylbenz(a)anthracene (DMBA)-induced mammary gland carcinogenesis. The estrogen receptor-positive human breast adenocarcinoma cell line (MCF-7) was used for in vitro analysis. This was combined with a network pharmacology-based approach to assess the anticancer properties of Spirulina (SP) extract and understand its molecular mechanisms. The results showed that the administration of 1 g/kg of SP increased the antioxidant activity by raising levels of catalase (CAT) and superoxide dismutase (SOD), while decreasing the levels of malonaldehyde (MDA) and protein carbonyl. A histological examination revealed reduced tumor occurrence, decreased estrogen receptor expression, suppressed cell proliferation, and promoted apoptosis in SP protected animals. In addition, SP disrupted the G2/M phase of the MCF-7 cell cycle, inducing apoptosis and reactive oxygen species (ROS) accumulation. It also enhanced intrinsic apoptosis in MCF-7 cells by upregulating cytochrome c, Bax, caspase-8, caspase-9, and caspase-7 proteins, while downregulating Bcl-2 production. The main compounds identified in the LC-MS/MS study of SP were 7-hydroxycoumarin derivatives of cinnamic acid, hinokinin, valeric acid, and α-linolenic acid. These substances specifically targeted three important proteins: ERK1/2 MAPK, PI3K-protein kinase B (AKT), and the epidermal growth factor receptor (EGFR). Network analysis and molecular docking indicated a significant binding affinity between SP and these proteins. This was verified by Western blot analysis that revealed decreased protein levels of p-EGFR, p-ERK1/2, and p-AKT following SP administration. SP was finally reported to suppress MCF-7 cell growth and induce apoptosis by modulating the PI3K/AKT/EGFR and MAPK signaling pathways suggesting EGFR as a potential target of SP in breast cancer (BC) treatment.

A review of deep learning methods for ligand based drug virtual screening

Drug discovery is costly and time consuming, and modern drug discovery endeavors are progressively reliant on computational methodologies, aiming to mitigate temporal and financial expenditures associated with the process. In particular, the time required for vaccine and drug discovery is prolonged during emergency situations such as the coronavirus 2019 pandemic. Recently, the performance of deep learning methods in drug virtual screening has been particularly prominent. It has become a concern for researchers how to summarize the existing deep learning in drug virtual screening, select different models for different drug screening problems, exploit the advantages of deep learning models, and further improve the capability of deep learning in drug virtual screening. This review first introduces the basic concepts of drug virtual screening, common datasets, and data representation methods. Then, large numbers of common deep learning methods for drug virtual screening are compared and analyzed. In addition, a dataset of different sizes is constructed independently to evaluate the performance of each deep learning model for the difficult problem of large-scale ligand virtual screening. Finally, the existing challenges and future directions in the field of virtual screening are presented.

PubChem synonym filtering process using crowdsourcing

PubChem ( https://pubchem.ncbi.nlm.nih.gov ) is a public chemical information resource containing more than 100 million unique chemical structures. One of the most requested tasks in PubChem and other chemical databases is to search chemicals by name (also commonly called a "chemical synonym"). PubChem performs this task by looking up chemical synonym-structure associations provided by individual depositors to PubChem. In addition, these synonyms are used for many purposes, including creating links between chemicals and PubMed articles (using Medical Subject Headings (MeSH) terms). However, these depositor-provided name-structure associations are subject to substantial discrepancies within and between depositors, making it difficult to unambiguously map a chemical name to a specific chemical structure. The present paper describes PubChem's crowdsourcing-based synonym filtering strategy, which resolves inter- and intra-depositor discrepancies in synonym-structure associations as well as in the chemical-MeSH associations. The PubChem synonym filtering process was developed based on the analysis of four crowd-voting strategies, which differ in the consistency threshold value employed (60% vs 70%) and how to resolve intra-depositor discrepancies (a single vote vs. multiple votes per depositor) prior to inter-depositor crowd-voting. The agreement of voting was determined at six levels of chemical equivalency, which considers varying isotopic composition, stereochemistry, and connectivity of chemical structures and their primary components. While all four strategies showed comparable results, Strategy I (one vote per depositor with a 60% consistency threshold) resulted in the most synonyms assigned to a single chemical structure as well as the most synonym-structure associations disambiguated at the six chemical equivalency contexts. Based on the results of this study, Strategy I was implemented in PubChem's filtering process that cleans up synonym-structure associations as well as chemical-MeSH associations. This consistency-based filtering process is designed to look for a consensus in name-structure associations but cannot attest to their correctness. As a result, it can fail to recognize correct name-structure associations (or incorrect ones), for example, when a synonym is provided by only one depositor or when many contributors are incorrect. However, this filtering process is an important starting point for quality control in name-structure associations in large chemical databases like PubChem.

A stacking-based algorithm for antifreeze protein identification using combined physicochemical, pseudo amino acid composition, and reduction property features

Antifreeze proteins have wide applications in the medical and food industries. In this study, we propose a stacking-based classifier that can effectively identify antifreeze proteins. Initially, feature extraction was performed in three aspects: reduction properties, scalable pseudo amino acid composition, and physicochemical properties. A hybrid feature set comprised of the combined information from these three categories was obtained. Subsequently, we trained the training set based on LightGBM, XGBoost, and RandomForest algorithms, and the training outcomes were passed to the Logistic algorithm for matching, thereby establishing a stacking algorithm. The proposed algorithm was tested on the test set and an independent validation set. Experimental data indicates that the algorithm achieved a recognition accuracy of 98.3 %, and an accuracy of 98.5 % on the validation set. Lastly, we analyzed the reasons why numerical features achieved high recognition capabilities from multiple aspects. Data dimensionality reduction and the analysis from two-dimensional and three-dimensional views revealed separability between positive and negative samples, and the protein three-dimensional structure further demonstrated significant differences in related features between the two samples. Analysis of the classifier revealed that Hr*Hr, HrHr, and Sc-PseAAC_1, 188D(152,116,57,183) were among the seven most important numerical features affecting algorithm recognition. For Hr*Hr and HrHr, supportive sequence level evidence for the reduction dictionary was found in terms of conservation area analysis, multiple sequence alignment, and amino acid conservative substitution. Moreover, the importance of the reduction dictionary was recognized through a comparative analysis of importance before and after the reduction, realizing the effectiveness of the dictionary in improving feature importance. A decision tree model has been utilized to discern the distinctions between dipeptides associated with the physical and chemical properties of His(H), Iso(I), Leu(L), and Lys(K) and other dipeptides. We finally analyzed the other seven features of importance, and data analysis confirmed that hydrophobicity, secondary structure, charge properties, van der Waals forces, and solvent accessibility are also factors affecting the antifreeze capability of proteins.

Exploring the anti-osteoporosis potential of Petroselinum crispum (Mill.) Fuss extract employing experimentally ovariectomized rat model and network pharmacology approach

One of the most prevalent secondary osteoporosis is ovariectomy-induced osteoporosis. Parsley (Petroselinum crispum) has potent estrogenic and antioxidant properties and was used traditionally in the treatment of amenorrhea and dysmenorrhea. The present study aimed to characterize parsley leaf extract (PLE) employing RP-HPLC-MS-MS/MS-based method and possible protective effect in ovariectomized (OVX)-induced osteoporosis in rats was assessed. Rats were randomly assigned into SHAM group, OVX group, PLE + OVX group (150 mg/kg/day, p.o), and estradiol benzoate (E2) + OVX group (30 μg/kg/day, s.c). After eight weeks following ovariectomy, biomarkers of bone strength, bone resorption, oxidative stress and histopathology were carried out. A network pharmacology approach investigated the key targets and potential mechanisms by of PLE metabolites against osteoporosis using databases: PubChem, BindingDB server, DisGeNET, ShinyGO, and KEGG Pathway. Moreover, FunRich 3.1.3, Cytoscape 3.10.0, and MOE 2019.0102 softwares were used for network pharmacology analysis and molecular docking studies. Flavones and hydroxycinnamic acid derivatives were predominant among 38 metabolites in PLE. It significantly restored bone strength and bone resorption biomarkers, osteocalcin (OST), oxidative stress biomarkers and histopathological alterations. The employed network pharmacology approach revealed that 14 primary target genes were associated with decreasing the severity of osteoporosis. Molecular docking revealed that cGMP-PKG signaling pathway has the highest fold enrichment and its downstream PDE5A. Luteolin, diosmetin, and isorhamnetin derivatives affected mostly osteoporosis targets. PLE exhibited protective action against ovariectomy-induced osteoporosis in rats and may be a promising therapy for premenopausal bone loss. cGMP-PKG signaling pathway could be a promising target for PLE in treating osteoporosis.

Current strategies using 3D organoids to establish in vitro maternal-embryonic interaction

IMPORTANCE

The creation of robust maternal-embryonic interactions and implantation models is important for comprehending the early stages of embryonic development and reproductive disorders. Traditional two-dimensional (2D) cell culture systems often fail to accurately mimic the highly complex in vivo conditions. The employment of three-dimensional (3D) organoids has emerged as a promising strategy to overcome these limitations in recent years. The advancements in the field of organoid technology have opened new avenues for studying the physiology and diseases affecting female reproductive tract.

OBSERVATIONS

This review summarizes the current strategies and advancements in the field of 3D organoids to establish maternal-embryonic interaction and implantation models for use in research and personalized medicine in assisted reproductive technology. The concepts of endometrial organoids, menstrual blood flow organoids, placental trophoblast organoids, stem cell-derived blastoids, and in vitro-generated embryo models are discussed in detail. We show the incorportaion of organoid systems and microfluidic technology to enhance tissue performance and precise management of the cellular surroundings.

CONCLUSIONS AND RELEVANCE

This review provides insights into the future direction of modeling maternal-embryonic interaction research and its combination with other powerful technologies to interfere with this dialogue either by promoting or hindering it for improving fertility or methods for contraception, respectively. The merging of organoid systems with microfluidics facilitates the creation of sophisticated and functional organoid models, enhancing insights into organ development, disease mechanisms, and personalized medical investigations.

Network pharmacology and molecular docking to elucidate the common mechanism of hydroxychloroquine treatment in lupus nephritis and IgA nephropathy

OBJECTIVE

Hydroxychloroquine (HCQ), characterized by a broad effect on immune regulation, has been widely used in the treatment of autoimmune glomerulonephritis such as lupus nephritis (LN) and immunoglobulin A nephropathy (IgAN). The current research investigates whether HCQ plays a role in the treatment of LN and IgAN through common mechanisms since the pathogenesis of both LN and IgAN is closely related to immune complex deposition, complement activation, and ultimately inflammation.

METHODS

Seventy-two common targets were obtained related to the common mechanism of HCQ treatment of LN and IgAN. Targets associated with LN and IgAN were collected based on DisGeNET, GeneCards, and OMIM databases. Possible HCQ targets were obtained from the PubChem database and PharmMapper databases. The overlapping targets of HCQ ingredients, IgAN, and LN were discovered via the Venn 2.1.0 online platform. Through the DAVID database, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Cytoscape (v3.9.1) was used to build a protein-protein interaction (PPI) network. Molecular docking was performed by using AutoDockTools 1.5.6 software and PyMol software to match the binding activity between HCQ and the 10 core targets.

RESULTS

The results showed that core targets (including MMP 2, PPARG, IL-2, MAPK14, MMP 9, and SRC), three signaling pathways (including the PI3K-Akt, AGE-RAGE, and MAPK), and cell differentiation (including Th1, Th2, and Th17) might be related to the body's immunity and inflammation. These results suggested that HCQ might act on targets and pathways involved in inflammation and immune regulation to exert a common effect on the treatment of LN and IgAN.

CONCLUSIONS

The current study provided new evidence for the protective mechanism and clinical utility of HCQ against LN and IgAN.

Can Small Molecules Provide Clues on Disease Progression in Cerebrospinal Fluid from Mild Cognitive Impairment and Alzheimer's Disease Patients?

Alzheimer's disease (AD) is a complex and multifactorial neurodegenerative disease, which is currently diagnosed via clinical symptoms and nonspecific biomarkers (such as Aβ1-42, t-Tau, and p-Tau) measured in cerebrospinal fluid (CSF), which alone do not provide sufficient insights into disease progression. In this pilot study, these biomarkers were complemented with small-molecule analysis using non-target high-resolution mass spectrometry coupled with liquid chromatography (LC) on the CSF of three groups: AD, mild cognitive impairment (MCI) due to AD, and a non-demented (ND) control group. An open-source cheminformatics pipeline based on MS-DIAL and patRoon was enhanced using CSF- and AD-specific suspect lists to assist in data interpretation. Chemical Similarity Enrichment Analysis revealed a significant increase of hydroxybutyrates in AD, including 3-hydroxybutanoic acid, which was found at higher levels in AD compared to MCI and ND. Furthermore, a highly sensitive target LC-MS method was used to quantify 35 bile acids (BAs) in the CSF, revealing several statistically significant differences including higher dehydrolithocholic acid levels and decreased conjugated BA levels in AD. This work provides several promising small-molecule hypotheses that could be used to help track the progression of AD in CSF samples.

Cellulose nanofibers produced from spaghetti squash peel by deep eutectic solvents and ultrasonication

In this study, the cellulose nanofibers (CNFs) derived from spaghetti squash peel (SSP) were prepared using a novel approach involving deep eutectic solvent (DES) pretreatment coupled with ultrasonication. Molecular dynamics (MD) simulations revealed that the number of hydrogen bonds influences the viscosity and density of DES systems, and experimental viscosity (ηexp) confirmed consistency with the computed viscosity (ηMD) trends. After DES pretreatment and ultrasonication, the cellulose content of ChCl/oxalic acid (ChCl/OA) CNF (35.63%) and ChCl/formic acid (ChCl/FA) (32.46%) is higher than ChCl/Urea CNF (28.27%). The widths of ChCl/OA CNF, ChCl/FA CNF, and ChCl/Urea CNF were 19.83, 11.34, and 18.27 nm, respectively, showing a network-like fiber distribution. Compared with SSP (29.76%) and non-ultrasonic samples, the crystallinity index of ChCl/OA CNF, ChCl/FA CNF, and ChCl/Urea CNF was improved by ultrasonication. The thermal decomposition residue of ChCl/OA CNF (25.54%), ChCl/FA CNF (18.54%), and ChCl/Urea CNF (23.62%) was lower than that of SSP (29.57%). These results demonstrate that CNFs can be prepared from SSP via DES pretreatment combined with ultrasonication. The lowest viscosity observed in the formic acid DES group (ηexp of 18 mPa·s), the ChCl/FA CNF exhibits excellent stability (Zeta potential of -37.6 mV), which can provide a promising prospect for utilization in biomass by-products and applications in the materials field.

Screening of Prospective Antiallergic Compound as FcεRI Inhibitors and Its Antiallergic Efficacy Through Immunoinformatics Approaches

The occurrence of allergy, a type I hypersensitivity reaction, is rising exponentially all over the world. Sometimes, allergy proves to be fatal for atopic patients, due to the occurrence of anaphylaxis. This study is aimed to find an anti-allergic agent that can inhibit the binding of IgE to Human High Affinity IgE Receptor (FCεRI), thereby preventing the degranulation of mast cells. A considerable number of potential anti-allergic compounds were assessed for their inhibitory strength through ADMET studies. AUTODOCK was used for estimating the binding energy between anti-allergic compounds and FCεRI, along with the interacting amino acids. The docked pose showing favorable binding energy was subjected to molecular dynamics simulation study. Marrubiin, a diterpenoid lactone from Lamiaceae, and epicatechin-3-gallate appears to be effective in blocking the Human High Affinity IgE Receptor (FCεRI). This in-silico study proposes the use of marrubiin and epicatechin-3-gallate, in the downregulation of allergic responses. Due to the better inhibition constant, future direction of this study is to analyze the safety and efficacy of marrubiin in anti-allergic activities through in-vivo clinical human trials.

Chia seeds oil ameliorate chronic immobilization stress-induced neurodisturbance in rat brains via activation of the antioxidant/anti-inflammatory/antiapoptotic signaling pathways

Chronic immobilization stress plays a key role in several neuropsychiatric disorders. This investigation assessed the possible ameliorative effect of chia seed oil (CSO) against the neurodisturbance-induced in rats by chronic immobilization. Rats were randomly allocated into control, CSO (1 ml/kg b.wt./orally), restrained (6 h/day), CSO pre-restraint, and CSO post-restraint for 60 days. Results revealed a significant reduction in serum corticosterone level, gene expression of corticotrophin-releasing factor, pro-inflammatory cytokines, and oxidative biomarkers in restrained rats treated with CSO. The histopathological findings revealed restoring necrosis and neuronal loss in CSO-treated-restraint rats. The immunohistochemical evaluation revealed a significant reduction in the immuno-expression of caspase-3, nuclear factor kappa B, interleukin-6, and cyclooxygenase-2 (COX-2), and an elevation of calbindin-28k and synaptophysin expression compared to non-treated restraint rats. The molecular docking showed the CSO high affinity for several target proteins, including caspase-3, COX-2, corticotropin-releasing hormone binding protein, corticotropin-releasing factor receptors 1 and 2, interleukin-1 receptor types 1 and 2, interleukin-6 receptor subunits alpha and beta. In conclusion, CSO emerges as a promising candidate against stress-induced brain disruptions by suppressing inflammatory/oxidative/apoptotic signaling pathways due to its numerous antioxidant and anti-inflammatory components, mainly α-linolenic acid. Future studies are necessary to evaluate the CSO therapeutic impacts in human neurodisturbances.

Traditional Chinese Medicine studies for Alzheimer's disease via network pharmacology based on entropy and random walk

Alzheimer's disease (AD) is a common neurodegenerative disease having complex pathogenesis, approved drugs can only alleviate symptoms of AD for a period of time. Traditional Chinese medicine (TCM) contains multiple active ingredients that can act on multiple targets simultaneously. In this paper, a novel algorithm based on entropy and random walk with the restart of heterogeneous network (RWRHE) is proposed for predicting active ingredients for AD and screening out the effective TCMs for AD. First, Six TCM compounds containing 20 herbs from the AD drug reviews in the CNKI (China National Knowledge Internet) are collected, their active ingredients and targets are retrieved from different databases. Then, comprehensive similarity networks of active ingredients and targets are constructed based on different aspects and entropy weight, respectively. A comprehensive heterogeneous network is constructed by integrating the known active ingredient-target association information and two comprehensive similarity networks. Subsequently, bi-random walks are applied on the heterogeneous network to predict active ingredient-target associations. AD related targets are selected as the seed nodes, a random walk is carried out on the target similarity network to predict the AD-target associations, and the associations of AD-active ingredients are inferred and scored. The effective herbs and compounds for AD are screened out based on their active ingredients' scores. The results measured by machine learning and bioinformatics show that the RWRHE algorithm achieves better prediction accuracy, the top 15 active ingredients may act as multi-target agents in the prevention and treatment of AD, Danshen, Gouteng and Chaihu are recommended as effective TCMs for AD, Yiqitongyutang is recommended as effective compound for AD.

Unlocking Potentially Therapeutic Phytochemicals in Capadulla (Doliocarpus dentatus) from Guyana Using Untargeted Mass Spectrometry-Based Metabolomics

Doliocarpus dentatus is thought to have a wide variety of therapeutic phytochemicals that allegedly improve libido and cure impotence. Although a few biomarkers have been identified with potential antinociceptive and cytotoxic properties, an untargeted mass spectrometry-based metabolomics approach has never been undertaken to identify therapeutic biofingerprints for conditions, such as erectile dysfunction, in men. This study executes a preliminary phytochemical screening of the woody vine of two ecotypes of D. dentatus with renowned differences in therapeutic potential for erectile dysfunction. Liquid chromatography-mass spectrometry-based metabolomics was used to screen for flavonoids, terpenoids, and other chemical classes found to contrast between red and white ecotypes. Among the metabolite chemodiversity found in the ecotype screens, using a combination of GNPS, MS-DIAL, and SIRIUS, approximately 847 compounds were annotated at levels 2 to 4, with the majority of compounds falling under lipid and lipid-like molecules, benzenoids and phenylpropanoids, and polyketides, indicative of the contributions of the flavonoid, shikimic acid, and terpenoid biosynthesis pathways. Despite the extensive annotation, we report on 138 tentative compound identifications of potentially therapeutic compounds, with 55 selected compounds at a level-2 annotation, and 22 statistically significant therapeutic biomarkers, the majority of which were polyphenols. Epicatechin methyl gallate, catechin gallate, and proanthocyanidin A2 had the greatest significant differences and were also relatively abundant among the red and white ecotypes. These putatively identified compounds reportedly act as antioxidants, neutralizing damaging free radicals, and lowering cell oxidative stress, thus aiding in potentially preventing cellular damage and promoting overall well-being, especially for treating erectile dysfunction (ED).

DECIMER.ai: an open platform for automated optical chemical structure identification, segmentation and recognition in scientific publications

The number of publications describing chemical structures has increased steadily over the last decades. However, the majority of published chemical information is currently not available in machine-readable form in public databases. It remains a challenge to automate the process of information extraction in a way that requires less manual intervention - especially the mining of chemical structure depictions. As an open-source platform that leverages recent advancements in deep learning, computer vision, and natural language processing, DECIMER.ai (Deep lEarning for Chemical IMagE Recognition) strives to automatically segment, classify, and translate chemical structure depictions from the printed literature. The segmentation and classification tools are the only openly available packages of their kind, and the optical chemical structure recognition (OCSR) core application yields outstanding performance on all benchmark datasets. The source code, the trained models and the datasets developed in this work have been published under permissive licences. An instance of the DECIMER web application is available at https://decimer.ai .

Metabolomic analysis reveals a differential adaptation process of the larval stages of Anisakis simplex to the host environment

Introduction: Anisakis simplex are parasitic nematodes that cause anisakiasis. The possibility of infection with this parasite is through consumption of raw or undercooked fish products. A. simplex infections are often misdiagnosed, especially in subclinical cases that do not present with typical symptoms such as urticaria, angioedema, and gastrointestinal allergy. The resulting allergic reactions range from rapid-onset and potentially fatal anaphylactic reactions to chronic, debilitating conditions. While there have been numerous published studies on the genomes and proteomes of A. simplex, less attention has been paid to the metabolomes. Metabolomics is concerned with the composition of metabolites in biological systems. Dynamic responses to endogenous and exogenous stimuli are particularly well suited for the study of holistic metabolic responses. In addition, metabolomics can be used to determine metabolic activity at different stages of development or during growth. Materials and methods: In this study, we reveal for the first time the metabolomes of infectious stages (L3 and L4) of A. simplex using untargeted metabolomics by ultra-performance liquid chromatography-mass spectrometry. Results: In the negative ionization mode (ESI-), we identified 172 different compounds, whereas in the positive ionization mode (ESI+), 186 metabolites were found. Statistical analysis showed that 60 metabolites were found in the ESI- mode with different concentration in each group, of which 21 were more enriched in the L3 larvae and 39 in the L4 stage of A. simplex. Comparison of the individual developmental stages in the ESI + mode also revealed a total of 60 differential metabolites, but 32 metabolites were more enriched in the L3 stage larvae, and 28 metabolites were more concentrated in the L4 stage. Discussion: The metabolomics study revealed that the developmental stages of A. simplex differed in a number of metabolic pathways, including nicotinate and nicotinamide metabolism. In addition, molecules responsible for successful migration within their host, such as pyridoxine and prostaglandins (E1, E2, F1a) were present in the L4 stage. In contrast, metabolic pathways for amino acids, starch, and sucrose were mainly activated in the L3 stage. Our results provide new insights into the comparative metabolome profiles of two different developmental stages of A. simplex.

Bridging glycoinformatics and cheminformatics: integration efforts between GlyCosmos and PubChem

The GlyCosmos Glycoscience Portal (https://glycosmos.org) and PubChem (https://pubchem.ncbi.nlm.nih.gov/) are major portals for glycoscience and chemistry, respectively. GlyCosmos is a portal for glycan-related repositories, including GlyTouCan, GlycoPOST, and UniCarb-DR, as well as for glycan-related data resources that have been integrated from a variety of 'omics databases. Glycogenes, glycoproteins, lectins, pathways, and disease information related to glycans are accessible from GlyCosmos. PubChem, on the other hand, is a chemistry-based portal at the National Center for Biotechnology Information. PubChem provides information not only on chemicals, but also genes, proteins, pathways, as well as patents, bioassays, and more, from hundreds of data resources from around the world. In this work, these 2 portals have made substantial efforts to integrate their complementary data to allow users to cross between these 2 domains. In addition to glycan structures, key information, such as glycan-related genes, relevant diseases, glycoproteins, and pathways, was integrated and cross-linked with one another. The interfaces were designed to enable users to easily find, access, download, and reuse data of interest across these resources. Use cases are described illustrating and highlighting the type of content that can be investigated. In total, these integrations provide life science researchers improved awareness and enhanced access to glycan-related information.

Integrating metabolomics and network pharmacology to assess the effects of quercetin on lung inflammatory injury induced by human respiratory syncytial virus

Quercetin (QR) has significant anti-respiratory syncytial virus (RSV) effects. However, its therapeutic mechanism has not been thoroughly explored. In this study, a lung inflammatory injury model caused by RSV was established in mice. Untargeted lung tissue metabolomics was used to identify differential metabolites and metabolic pathways. Network pharmacology was used to predict potential therapeutic targets of QR and analyze biological functions and pathways modulated by QR. By overlapping the results of the metabolomics and the network pharmacology analyses, the common targets of QR that were likely to be involved in the amelioration of RSV-induced lung inflammatory injury by QR were identified. Metabolomics analysis identified 52 differential metabolites and 244 corresponding targets, while network pharmacology analysis identified 126 potential targets of QR. By intersecting these 244 targets with the 126 targets, hypoxanthine-guanine phosphoribosyltransferase (HPRT1), thymidine phosphorylase (TYMP), lactoperoxidase (LPO), myeloperoxidase (MPO), and cytochrome P450 19A1 (CYP19A1) were identified as the common targets. The key targets, HPRT1, TYMP, LPO, and MPO, were components of purine metabolic pathways. The present study demonstrated that QR effectively ameliorated RSV-induced lung inflammatory injury in the established mouse model. Combining metabolomics and network pharmacology showed that the anti-RSV effect of QR was closely associated with purine metabolism pathways.

Open data and algorithms for open science in AI-driven molecular informatics

Recent years have seen a sharp increase in the development of deep learning and artificial intelligence-based molecular informatics. There has been a growing interest in applying deep learning to several subfields, including the digital transformation of synthetic chemistry, extraction of chemical information from the scientific literature, and AI in natural product-based drug discovery. The application of AI to molecular informatics is still constrained by the fact that most of the data used for training and testing deep learning models are not available as FAIR and open data. As open science practices continue to grow in popularity, initiatives which support FAIR and open data as well as open-source software have emerged. It is becoming increasingly important for researchers in the field of molecular informatics to embrace open science and to submit data and software in open repositories. With the advent of open-source deep learning frameworks and cloud computing platforms, academic researchers are now able to deploy and test their own deep learning models with ease. With the development of new and faster hardware for deep learning and the increasing number of initiatives towards digital research data management infrastructures, as well as a culture promoting open data, open source, and open science, AI-driven molecular informatics will continue to grow. This review examines the current state of open data and open algorithms in molecular informatics, as well as ways in which they could be improved in future.

Repurposing FIASMAs against Acid Sphingomyelinase for COVID-19: A Computational Molecular Docking and Dynamic Simulation Approach

Over the past few years, COVID-19 has caused widespread suffering worldwide. There is great research potential in this domain and it is also necessary. The main objective of this study was to identify potential inhibitors against acid sphingomyelinase (ASM) in order to prevent coronavirus infection. Experimental studies revealed that SARS-CoV-2 causes activation of the acid sphingomyelinase/ceramide pathway, which in turn facilitates the viral entry into the cells. The objective was to inhibit acid sphingomyelinase activity in order to prevent the cells from SARS-CoV-2 infection. Previous studies have reported functional inhibitors against ASM (FIASMAs). These inhibitors can be exploited to block the entry of SARS-CoV-2 into the cells. To achieve our objective, a drug library containing 257 functional inhibitors of ASM was constructed. Computational molecular docking was applied to dock the library against the target protein (PDB: 5I81). The potential binding site of the target protein was identified through structural alignment with the known binding pocket of a protein with a similar function. AutoDock Vina was used to carry out the docking steps. The docking results were analyzed and the inhibitors were screened based on their binding affinity scores and ADME properties. Among the 257 functional inhibitors, Dutasteride, Cepharanthine, and Zafirlukast presented the lowest binding affinity scores of −9.7, −9.6, and −9.5 kcal/mol, respectively. Furthermore, computational ADME analysis of these results revealed Cepharanthine and Zafirlukast to have non-toxic properties. To further validate these findings, the top two inhibitors in complex with the target protein were subjected to molecular dynamic simulations at 100 ns. The molecular interactions and stability of these compounds revealed that these inhibitors could be a promising tool for inhibiting SARS-CoV-2 infection.

Computational Approaches Drive Developments in Immune-Oncology Therapies for PD-1/PD-L1 Immune Checkpoint Inhibitors

Computational approaches in immune-oncology therapies focus on using data-driven methods to identify potential immune targets and develop novel drug candidates. In particular, the search for PD-1/PD-L1 immune checkpoint inhibitors (ICIs) has enlivened the field, leveraging the use of cheminformatics and bioinformatics tools to analyze large datasets of molecules, gene expression and protein-protein interactions. Up to now, there is still an unmet clinical need for improved ICIs and reliable predictive biomarkers. In this review, we highlight the computational methodologies applied to discovering and developing PD-1/PD-L1 ICIs for improved cancer immunotherapies with a greater focus in the last five years. The use of computer-aided drug design structure- and ligand-based virtual screening processes, molecular docking, homology modeling and molecular dynamics simulations methodologies essential for successful drug discovery campaigns focusing on antibodies, peptides or small-molecule ICIs are addressed. A list of recent databases and web tools used in the context of cancer and immunotherapy has been compilated and made available, namely regarding a general scope, cancer and immunology. In summary, computational approaches have become valuable tools for discovering and developing ICIs. Despite significant progress, there is still a need for improved ICIs and biomarkers, and recent databases and web tools have been compiled to aid in this pursuit.

Database resources of the National Center for Biotechnology Information in 2023

The National Center for Biotechnology Information (NCBI) provides online information resources for biology, including the GenBank® nucleic acid sequence database and the PubMed® database of citations and abstracts published in life science journals. NCBI provides search and retrieval operations for most of these data from 35 distinct databases. The E-utilities serve as the programming interface for most of these databases. New resources include the Comparative Genome Resource (CGR) and the BLAST ClusteredNR database. Resources receiving significant updates in the past year include PubMed, PMC, Bookshelf, IgBLAST, GDV, RefSeq, NCBI Virus, GenBank type assemblies, iCn3D, ClinVar, GTR, dbGaP, ALFA, ClinicalTrials.gov, Pathogen Detection, antimicrobial resistance resources, and PubChem. These resources can be accessed through the NCBI home page at https://www.ncbi.nlm.nih.gov.

PubChem 2023 update

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a popular chemical information resource that serves a wide range of use cases. In the past two years, a number of changes were made to PubChem. Data from more than 120 data sources was added to PubChem. Some major highlights include: the integration of Google Patents data into PubChem, which greatly expanded the coverage of the PubChem Patent data collection; the creation of the Cell Line and Taxonomy data collections, which provide quick and easy access to chemical information for a given cell line and taxon, respectively; and the update of the bioassay data model. In addition, new functionalities were added to the PubChem programmatic access protocols, PUG-REST and PUG-View, including support for target-centric data download for a given protein, gene, pathway, cell line, and taxon and the addition of the 'standardize' option to PUG-REST, which returns the standardized form of an input chemical structure. A significant update was also made to PubChemRDF. The present paper provides an overview of these changes.

The NORMAN Suspect List Exchange (NORMAN-SLE): facilitating European and worldwide collaboration on suspect screening in high resolution mass spectrometry

Background

The NORMAN Association (https://www.norman-network.com/) initiated the NORMAN Suspect List Exchange (NORMAN-SLE; https://www.norman-network.com/nds/SLE/) in 2015, following the NORMAN collaborative trial on non-target screening of environmental water samples by mass spectrometry. Since then, this exchange of information on chemicals that are expected to occur in the environment, along with the accompanying expert knowledge and references, has become a valuable knowledge base for "suspect screening" lists. The NORMAN-SLE now serves as a FAIR (Findable, Accessible, Interoperable, Reusable) chemical information resource worldwide.

Results

The NORMAN-SLE contains 99 separate suspect list collections (as of May 2022) from over 70 contributors around the world, totalling over 100,000 unique substances. The substance classes include per- and polyfluoroalkyl substances (PFAS), pharmaceuticals, pesticides, natural toxins, high production volume substances covered under the European REACH regulation (EC: 1272/2008), priority contaminants of emerging concern (CECs) and regulatory lists from NORMAN partners. Several lists focus on transformation products (TPs) and complex features detected in the environment with various levels of provenance and structural information. Each list is available for separate download. The merged, curated collection is also available as the NORMAN Substance Database (NORMAN SusDat). Both the NORMAN-SLE and NORMAN SusDat are integrated within the NORMAN Database System (NDS). The individual NORMAN-SLE lists receive digital object identifiers (DOIs) and traceable versioning via a Zenodo community (https://zenodo.org/communities/norman-sle), with a total of > 40,000 unique views, > 50,000 unique downloads and 40 citations (May 2022). NORMAN-SLE content is progressively integrated into large open chemical databases such as PubChem (https://pubchem.ncbi.nlm.nih.gov/) and the US EPA's CompTox Chemicals Dashboard (https://comptox.epa.gov/dashboard/), enabling further access to these lists, along with the additional functionality and calculated properties these resources offer. PubChem has also integrated significant annotation content from the NORMAN-SLE, including a classification browser (https://pubchem.ncbi.nlm.nih.gov/classification/#hid=101).

Conclusions

The NORMAN-SLE offers a specialized service for hosting suspect screening lists of relevance for the environmental community in an open, FAIR manner that allows integration with other major chemical resources. These efforts foster the exchange of information between scientists and regulators, supporting the paradigm shift to the "one substance, one assessment" approach. New submissions are welcome via the contacts provided on the NORMAN-SLE website (https://www.norman-network.com/nds/SLE/).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12302-022-00680-6.