Per- and Polyfluoroalkyl Substances (PFAS) in PubChem: 7 Million and Growing.

Per- and polyfluoroalkyl substances (PFAS) are of high concern, with calls to regulate them as a class. In 2021, the Organisation for Economic Co-operation and Development (OECD) revised the definition of PFAS to include any chemical containing at least one saturated CF2 or CF3 moiety. The consequence is that one of the largest open chemical collections, PubChem, with 116 million compounds, now contains over 7 million PFAS under this revised definition. These numbers are several orders of magnitude higher than previously established PFAS lists (typically thousands of entries) and pose an incredible challenge to researchers and computational workflows alike. This article describes a dynamic, openly accessible effort to navigate and explore the >7 million PFAS and >21 million fluorinated compounds (September 2023) in PubChem by establishing the "PFAS and Fluorinated Compounds in PubChem" Classification Browser (or "PubChem PFAS Tree"). A total of 36500 nodes support browsing of the content according to several categories, including classification, structural properties, regulatory status, or presence in existing PFAS suspect lists. Additional annotation and associated data can be used to create subsets (and thus manageable suspect lists or databases) of interest for a wide range of environmental, regulatory, exposomics, and other applications.

A new approach to monitoring typical organophosphorus compounds (OPs) in environmental media: From database building to suspect screening.

Organophosphorus compounds (OPs) are widely used as flame retardants (FRs) and plasticizers, yet strategies for comprehensively screening of suspect OPs in environmental samples are still lacking. In this work, a neoteric, robust, and general suspect screening technique was developed to identify novel chemical exposures by use of ultra-high performance liquid chromatography-Q Exactive hybrid quadrupole-Orbitrap high resolution mass spectrometry (UHPLC-Q-Orbitrap HRMS). We firstly established a suspect chemical database which had 7,922 OPs with 4,686 molecular formulas, and then conducted suspect screening in n = 50 indoor dust samples, n = 76 sediment samples, and n = 111 water samples. By use of scoring criteria such as retention time prediction models, we successfully confirmed five compounds by comparison with their authentic standards, and prioritized three OPs candidates including a nitrogen/fluorine-containing compound, that is dimethyl {1H-indol-3-yl[3-(trifluoromethyl)anilino]methyl} phosphonate (DMITFMAMP). Given that the biodegradation half-life values in water (t1/2,w) of DMITFMAMP calculated by EPI Suite is 180 d, it is considered to be potentially persistent. This strategy shows promising potential in environmental pollution assessment, and can be expected to be widely used in future research.

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.

Design and experimental validation of the oxazole and thiazole derivatives as potential antivirals against of human cytomegalovirus.

QSAR studies of a set of previously synthesized azole derivatives tested against human cytomegalovirus (HCMV) were performed using the OCHEM web platform. The predictive ability of the classification models has a balanced accuracy (BA) of 73-79%. The validation of the models using an external test set proved that the models can be used to predict the activity of newly designed compounds with a reasonable accuracy within the applicability domain (BA = 76-83%). The models were applied to screen a virtual chemical library with expected activity of compounds against HCMV. The five most promising new compounds were identified, synthesized and their antiviral activities against HCMV were evaluated in vitro. Two of them showed some activity against the HCMV strain AD169. According to the results of docking analysis, the most promising biotarget associated with HCMV is DNA polymerase. The docking of the most active compounds 1 and 5 in the DNA polymerase active site shows calculated binding energies of -8.6 and -7.8 kcal/mol, respectively. The ligand's complexation was stabilized by the formation of hydrogen bonds and hydrophobic interactions with amino acids Lys60, Leu43, Ile49, Pro77, Asp134, Ile135, Val136, Thr62 and Arg137.

In Silico Design and Experimental Validation of Novel Oxazole Derivatives Against Varicella zoster virus.

Varicella zoster virus (VZV) infection causes severe disease such as chickenpox, shingles, and postherpetic neuralgia, often leading to disability. Reactivation of latent VZV is associated with a decrease in specific cellular immunity in the elderly and in patients with immunodeficiency. However, due to the limited efficacy of existing therapy and the emergence of antiviral resistance, it has become necessary to develop new and effective antiviral drugs for the treatment of diseases caused by VZV, particularly in the setting of opportunistic infections. The goal of this work is to identify potent oxazole derivatives as anti-VZV agents by machine learning, followed by their synthesis and experimental validation. Predictive QSAR models were developed using the Online Chemical Modeling Environment (OCHEM). Data on compounds exhibiting antiviral activity were collected from the ChEMBL and uploaded in the OCHEM database. The predictive ability of the models was tested by cross-validation, giving coefficient of determination q2 = 0.87-0.9. The validation of the models using an external test set proves that the models can be used to predict the antiviral activity of newly designed and known compounds with reasonable accuracy within the applicability domain (q2 = 0.83-0.84). The models were applied to screen a virtual chemical library with expected activity of compounds against VZV. The 7 most promising oxazole derivatives were identified, synthesized, and tested. Two of them showed activity against the VZV Ellen strain upon primary in vitro antiviral screening. The synthesized compounds may represent an interesting starting point for further development of the oxazole derivatives against VZV. The developed models are available online at OCHEM http://ochem.eu/article/145978 and can be used to virtually screen for potential compounds with anti-VZV activity.

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

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database at the U.S. National Institutes of Health. Visited by millions of users every month, it plays a role as a key chemical information resource for biomedical research communities. Data in PubChem is from hundreds of contributors and organized into multiple collections by record type. Among these are the Protein, Gene, Pathway, and Taxonomy data collections. Records in these collections contain information on chemicals related to a given biological target (i.e., protein, gene, pathway, or taxon), helping users to analyze and interpret the biological activity data of molecules. In addition, annotations about the biological targets are collected from authoritative or curated data sources and integrated into the four collections. The content can be programmatically accessed through PubChem's web service interfaces (including PUG View). A machine-readable representation of this content is also provided within PubChemRDF.

PubChem Periodic Table and Element Pages: Improving Access to Information on Chemical Elements from Authoritative Sources.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is one of the top five most visited chemistry web sites in the world, with more than five million unique users per month (as of March 2020). Many of these users are educators, undergraduate students, and graduate students at academic institutions. Therefore, PubChem has a great potential as an online resource for chemical education. This paper describes the PubChem Periodic Table and Element pages, which were recently introduced to celebrate the 150th anniversary of the periodic table. These services help users navigate the abundant chemical element data available within PubChem, while providing a convenient entry point to explore additional chemical content, such as biological activities and health and safety data available in PubChem Compound pages for specific elements and their isotopes. The PubChem Periodic Table and Element pages are also available as widgets, which enable web developers to display PubChem's element data on web pages they design. The elemental data can be downloaded in common file formats and imported into data analysis programs (e.g., spreadsheet software, like Microsoft Excel and Google Sheets, and computer scripts, such as python and R). Overall, the PubChem Periodic Table and Element pages improve access to chemical element data from authoritative sources.

ChemSkin Reference Chemical Database for the Development of an In Vitro Skin Irritation Test.

Since the animal test ban on cosmetics in the EU in 2013, alternative in vitro safety tests have been actively researched to replace in vivo animal tests. For the development and evaluation of a new test method, reference chemicals with quality in vivo data are essential to assess the predictive capacity and applicability domain. Here, we compiled a reference chemical database (ChemSkin DB) for the development and evaluation of new in vitro skin irritation tests. The first candidates were selected from 317 chemicals (source data n = 1567) searched from the literature from the last 20 years, including previous validation study reports, ECETOC, and published papers. Chemicals showing inconsistent classification or those that were commercially unavailable, difficult or dangerous to handle, prohibitively expensive, or without quality in vivo or in vitro data were removed, leaving a total of 100 chemicals. Supporting references, in vivo Draize scores, UN GHS/EU CLP classifications and commercial sources were compiled. Test results produced by the approved methods of OECD Test No. 439 were included and compared using the classification table, scatter plot, and Pearson correlation analysis to identify the false predictions and differences between in vitro skin irritation tests. These results may provide an insight into the future development of new in vitro skin irritation tests.

FAIR chemical structures in the Journal of Cheminformatics.

The ability to access chemical information openly is an essential part of many scientific disciplines. The Journal of Cheminformatics is leading the way for rigorous, open cheminformatics in many ways, but there remains room for improvement in primary areas. This letter discusses how both authors and the journal alike can help increase the FAIRness (Findability, Accessibility, Interoperability, Reusability) of the chemical structural information in the journal. A proposed chemical structure template can serve as an interoperable Additional File format (already accessible), made more findable by linking the DOI of this data file to the article DOI metadata, supporting further reuse.

Empowering large chemical knowledge bases for exposomics: PubChemLite meets MetFrag.

Compound (or chemical) databases are an invaluable resource for many scientific disciplines. Exposomics researchers need to find and identify relevant chemicals that cover the entirety of potential (chemical and other) exposures over entire lifetimes. This daunting task, with over 100 million chemicals in the largest chemical databases, coupled with broadly acknowledged knowledge gaps in these resources, leaves researchers faced with too much-yet not enough-information at the same time to perform comprehensive exposomics research. Furthermore, the improvements in analytical technologies and computational mass spectrometry workflows coupled with the rapid growth in databases and increasing demand for high throughput "big data" services from the research community present significant challenges for both data hosts and workflow developers. This article explores how to reduce candidate search spaces in non-target small molecule identification workflows, while increasing content usability in the context of environmental and exposomics analyses, so as to profit from the increasing size and information content of large compound databases, while increasing efficiency at the same time. In this article, these methods are explored using PubChem, the NORMAN Network Suspect List Exchange and the in silico fragmentation approach MetFrag. A subset of the PubChem database relevant for exposomics, PubChemLite, is presented as a database resource that can be (and has been) integrated into current workflows for high resolution mass spectrometry. Benchmarking datasets from earlier publications are used to show how experimental knowledge and existing datasets can be used to detect and fill gaps in compound databases to progressively improve large resources such as PubChem, and topic-specific subsets such as PubChemLite. PubChemLite is a living collection, updating as annotation content in PubChem is updated, and exported to allow direct integration into existing workflows such as MetFrag. The source code and files necessary to recreate or adjust this are jointly hosted between the research parties (see data availability statement). This effort shows that enhancing the FAIRness (Findability, Accessibility, Interoperability and Reusability) of open resources can mutually enhance several resources for whole community benefit. The authors explicitly welcome additional community input on ideas for future developments.

Cheminformatics in the Identification of Drug Classes for the Treatment of Type 2 Diabetes.

Computer-Aided Drug Design has developed into a powerful suite of methods that complement experimental approaches to the identification of new pharmacologically active compounds. In particular, virtual screening has become a standard tool for lead identification. Diverse examples of the application of virtual screening applied to T2DM target proteins have been reported. While several of these indicate successful identification of new lead compounds from synthetic chemical and natural product databases, many of them have been performed on a small scale and with limited validation. Careful study design and collaboration with cheminformaticians and computational chemists will enable these approaches to fulfil their potential for T2DM.

Hybrid Design of Isonicotinic Acid Hydrazide Derivatives: Machine Learning Studies, Synthesis and Biological Evaluation of their Antituberculosis Activity.

BACKGROUND: Tuberculosis (TB) is an infection disease caused by Mycobacterium tuberculosis (Mtb) bacteria. One of the main causes of mortality from TB is the problem of Mtb resistance to known drugs. OBJECTIVE: The goal of this work is to identify potent small molecule anti-TB agents by machine learning, synthesis and biological evaluation. METHODS: The On-line Chemical Database and Modeling Environment (OCHEM) was used to build predictive machine learning models. Seven compounds were synthesized and tested in vitro for their antitubercular activity against H37Rv and resistant Mtb strains. RESULTS: A set of predictive models was built with OCHEM based on a set of previously synthesized isoniazid (INH) derivatives containing a thiazole core and tested against Mtb. The predictive ability of the models was tested by a 5-fold cross-validation, and resulted in balanced accuracies (BA) of 61-78% for the binary classifiers. Test set validation showed that the models could be instrumental in predicting anti- TB activity with a reasonable accuracy (with BA = 67-79 %) within the applicability domain. Seven designed compounds were synthesized and demonstrated activity against both the H37Rv and multidrugresistant (MDR) Mtb strains resistant to rifampicin and isoniazid. According to the acute toxicity evaluation in Daphnia magna neonates, six compounds were classified as moderately toxic (LD50 in the range of 10-100 mg/L) and one as practically harmless (LD50 in the range of 100-1000 mg/L). CONCLUSION: The newly identified compounds may represent a starting point for further development of therapies against Mtb. The developed models are available online at OCHEM http://ochem.eu/article/11 1066 and can be used to virtually screen for potential compounds with anti-TB activity.

A review of ligand-based virtual screening web tools and screening algorithms in large molecular databases in the age of big data.

Virtual screening has become a widely used technique for helping in drug discovery processes. The key to this success is its ability to aid in the identification of novel bioactive compounds by screening large molecular databases. Several web servers have emerged in the last few years supplying platforms to guide users in screening publicly accessible chemical databases in a reasonable time. In this review, we discuss a representative set of online virtual screening servers and their underlying similarity algorithms. Other related topics, such as molecular representation or freely accessible databases are also treated. The most relevant contributions to this review arise from critical discussions concerning the pros and cons of servers and algorithms, and the challenges that future works must solve in a virtual screening framework.

Hemostatic chemical constituents from natural medicine Toddalia asiatica root bark by LC-ESI Q-TOF MSE.

BACKGROUND: Toddalia asiatica root bark as an effective hemostatic natural medicine or Chinese materia medica was applied in China for long history, its complex drug action mechanisms and unclear substance basis have been constraining the development of this drug. RESULTS: An intelligentized strategy by LC-ESI Q-TOF MSE was presented in this study for rapid identification of hemostatic chemical constituents from this natural medicine. Chromatographic separation was performed on a C18 column (150 mm × 2.1 mm, 1.8 µm), the MSE data in both negative and positive ion modes were acquired to record the high-accuracy MS and MS/MS data of all precursor ions. To reduce the false positive identifications, structural confirmation was conducted by comparison with the isolated reference standards (tR and MS, MS/MS data) or matching with natural product databases. Bioassay-guided fractionation of the extract of T. asiatica root bark was also carried out. CONCLUSIONS: As a consequence, 31 natural compounds in T. asiatica root bark got putatively characterized. There were four main coumarins, isopimpinellin (Cp.23), pimpinellin (Cp.24), coumurrayin (Cp.30) and phellopterin (Cp.34) isolated and identified from T. asiatica root bark. The present study provided candidate strategy that helps to effectively identify the primary natural compounds of TCM or other complex natural medicines, and then promote development and application of natural medicines and their medicinal resources.

Wikipedia Chemical Structure Explorer: substructure and similarity searching of molecules from Wikipedia.

BACKGROUND: Wikipedia, the world's largest and most popular encyclopedia is an indispensable source of chemistry information. It contains among others also entries for over 15,000 chemicals including metabolites, drugs, agrochemicals and industrial chemicals. To provide an easy access to this wealth of information we decided to develop a substructure and similarity search tool for chemical structures referenced in Wikipedia. RESULTS: We extracted chemical structures from entries in Wikipedia and implemented a web system allowing structure and similarity searching on these data. The whole search as well as visualization system is written in JavaScript and therefore can run locally within a web page and does not require a central server. The Wikipedia Chemical Structure Explorer is accessible on-line at www.cheminfo.org/wikipedia and is available also as an open source project from GitHub for local installation. CONCLUSIONS: The web-based Wikipedia Chemical Structure Explorer provides a useful resource for research as well as for chemical education enabling both researchers and students easy and user friendly chemistry searching and identification of relevant information in Wikipedia. The tool can also help to improve quality of chemical entries in Wikipedia by providing potential contributors regularly updated list of entries with problematic structures. And last but not least this search system is a nice example of how the modern web technology can be applied in the field of cheminformatics. Graphical abstractWikipedia Chemical Structure Explorer allows substructure and similarity searches on molecules referenced in Wikipedia.

Current Challenges in Development of a Database of Three-Dimensional Chemical Structures.

We are developing a database named 3DMET, a three-dimensional structure database of natural metabolites. There are two major impediments to the creation of 3D chemical structures from a set of planar structure drawings: the limited accuracy of computer programs and insufficient human resources for manual curation. We have tested some 2D-3D converters to convert 2D structure files from external databases. These automatic conversion processes yielded an excessive number of improper conversions. To ascertain the quality of the conversions, we compared IUPAC Chemical Identifier and canonical SMILES notations before and after conversion. Structures whose notations correspond to each other were regarded as a correct conversion in our present work. We found that chiral inversion is the most serious factor during the improper conversion. In the current stage of our database construction, published books or articles have been resources for additions to our database. Chemicals are usually drawn as pictures on the paper. To save human resources, an optical structure reader was introduced. The program was quite useful but some particular errors were observed during our operation. We hope our trials for producing correct 3D structures will help other developers of chemical programs and curators of chemical databases.

Model for high-throughput screening of drug immunotoxicity--study of the anti-microbial G1 over peritoneal macrophages using flow cytometry.

Quantitative Structure-Activity (mt-QSAR) techniques may become an important tool for prediction of cytotoxicity and High-throughput Screening (HTS) of drugs to rationalize drug discovery process. In this work, we train and validate by the first time mt-QSAR model using TOPS-MODE approach to calculate drug molecular descriptors and Linear Discriminant Analysis (LDA) function. This model correctly classifies 8258 out of 9000 (Accuracy = 91.76%) multiplexing assay endpoints of 7903 drugs (including both train and validation series). Each endpoint correspond to one out of 1418 assays, 36 molecular and cellular targets, 46 standard type measures, in two possible organisms (human and mouse). After that, we determined experimentally, by the first time, the values of EC50 = 21.58 µg/mL and Cytotoxicity = 23.6% for the anti-microbial/anti-parasite drug G1 over Balb/C mouse peritoneal macrophages using flow cytometry. In addition, the model predicts for G1 only 7 positive endpoints out 1251 cytotoxicity assays (0.56% of probability of cytotoxicity in multiple assays). The results obtained complement the toxicological studies of this important drug. This work adds a new tool to the existing pool of few methods useful for multi-target HTS of ChEMBL and other libraries of compounds towards drug discovery.

WebChemDB: An Integrated Chemical Database Retrieval System

WebChemDB is an integrated chemical database retrieval system that provides access to over 8 million publicly available chemical structures, including related information on their biological activities and direct links to other public chemical resources, such as PubChem, ChEBI, and DrugBank. The data are publicly available over the web, using two-dimensional (2D) and three-dimensional (3D) structure retrieval systems with various filters and molecular descriptors. The web services API also provides researchers with functionalities to programmatically manipulate, search, and analyze the data.