CAS Common Chemistry in 2021: Expanding Access to Trusted Chemical Information for the Scientific Community

Easy-Amanida: An R Shiny application for the meta-analysis of aggregate results in clinical metabolomics using Amanida and Webchem

Meta-analysis is a useful tool in clinical research, as it combines the results of multiple clinical studies to improve precision when answering a particular scientific question. While there has been a substantial increase in publications using meta-analysis in various clinical research topics, the number of published meta-analyses in metabolomics is significantly lower compared to other omics disciplines. Metabolomics is the study of small chemical compounds in living organisms, which provides important insights into an organism's phenotype. However, the wide variety of compounds and the different experimental methods used in metabolomics make it challenging to perform a thorough meta-analysis. Additionally, there is a lack of consensus on reporting statistical estimates, and the high number of compound naming synonyms further complicates the process. Easy-Amanida is a new tool that combines two R packages, "amanida" and "webchem", to enable meta-analysis of aggregate statistical data, like p-value and fold-change, while ensuring the compounds naming harmonization. The Easy-Amanida app is implemented in Shiny, an R package add-on for interactive web apps, and provides a workflow to optimize the naming combination. This article describes all the steps to perform the meta-analysis using Easy-Amanida, including an illustrative example for interpreting the results. The use of aggregate statistics metrics extends the use of Easy-Amanida beyond the metabolomics field.

IPAD-DB: a manually curated database for experimentally verified inhibitors of proteins associated with Alzheimer's disease

Alzheimer's disease (AD) is a universal neurodegenerative disease with the feature of progressive dementia. Currently, there are only seven Food and Drug Administration-approved drugs for the treatment of AD, which merely offer temporary relief from symptom deterioration without reversing the underlying disease process. The identification of inhibitors capable of interacting with proteins associated with AD plays a pivotal role in the development of effective therapeutic interventions. However, a vast number of such inhibitors are dispersed throughout numerous published articles, rendering it inconvenient for researchers to explore potential drug candidates for AD. In light of this, we have manually compiled inhibitors targeting proteins associated with AD and constructed a comprehensive database known as IPAD-DB (Inhibitors of Proteins associated with Alzheimer's Disease Database). The curated inhibitors within this database encompass a diverse range of compounds, including natural compounds, synthetic compounds, drugs, natural extracts and nano-inhibitors. To date, the database has compiled >4800 entries, each representing a correspondent relationship between an inhibitor and its target protein. IPAD-DB offers a user-friendly interface that facilitates browsing, searching and downloading of its records. We firmly believe that IPAD-DB represents a valuable resource for screening potential AD drug candidates and investigating the underlying mechanisms of this debilitating disease. Access to IPAD-DB is freely available at http://www.lamee.cn/ipad-db/ and is compatible with all major web browsers. Database URL: http://www.lamee.cn/ipad-db/.

A machine learning driven automated system for safety data sheet indexing

Safety Data Sheets (SDS) are foundational to chemical management systems and are used in a wide variety of applications such as green chemistry, industrial hygiene, and regulatory compliance, among others within the Environment, Health, and Safety (EHS) and the Environment, Social, and Governance (ESG) domains. Companies usually prefer to have key pieces of information extracted from these datasheets and stored in an easy to access structured repository. This process is referred to as SDS "indexing". Historically, SDS indexing has always been done manually, which is labor-intensive, time-consuming, and costly. In this paper, we present an automated system to index the composition information of chemical products from SDS documents using a multi-stage ensemble method with a combination of machine learning models and rule-based systems stacked one after the other. The system specifically indexes the ingredient names, their corresponding Chemical Abstracts Service (CAS) numbers, and weight percentages. It takes the SDS document in PDF format as the input and gives the list of ingredient names along with their corresponding CAS numbers and weight percentages in a tabular format as the output. The system achieves a precision of 0.93 at the document level when evaluated on 20,000 SDS documents annotated for this purpose.

Multiscale computational simulation of pollutant behavior at water interfaces

The investigation of pollutant behavior at water interfaces is critical to understand pollution in aquatic systems. Computational methods allow us to overcome the limitations of experimental analysis, delivering valuable insights into the chemical mechanisms and structural characteristics of pollutant behavior at interfaces across a range of scales, from microscopic to mesoscopic. Quantum mechanics, all-atom molecular dynamics simulations, coarse-grained molecular dynamics simulations, and dissipative particle dynamics simulations represent diverse molecular interaction calculation methods that can effectively model pollutant behavior at environmental interfaces from atomic to mesoscopic scales. These methods provide a rich variety of information on pollutant interactions with water surfaces. This review synthesizes the advancements in applying typical computational methods to the formation, adsorption, binding, and catalytic conversion of pollutants at water interfaces. By drawing on recent advancements, we critically examine the current challenges and offer our perspective on future directions. This review seeks to advance our understanding of computational techniques for elucidating pollutant behavior at water interfaces, a critical aspect of water research.

Systematic Description of the Content Variation of Natural Products (NPs): To Prompt the Yield of High-Value NPs and the Discovery of New Therapeutics

Natural products (NPs) have long been associated with human production and play a key role in the survival of species. Significant variations in NP content may severely affect the "return on investment" of NP-based industries and render ecological systems vulnerable. Thus, it is crucial to construct a platform that relates variations in NP content to their corresponding mechanisms. In this study, a publicly accessible online platform, NPcVar (http://npcvar.idrblab.net/), was developed, which systematically described the variations of NP contents and their corresponding mechanisms. The platform comprises 2201 NPs and 694 biological resources, including plants, bacteria, and fungi, curated using 126 diverse factors with 26,425 records. Each record contains information about the species, NP, and factors involved, as well as NP content data, parts of the plant that produce NPs, the location of the experiment, and reference information. All factors were manually curated and categorized into 42 classes which belong to four mechanisms (molecular regulation, species factor, environmental condition, and combined factor). Additionally, the cross-links of species and NP to well-established databases and the visualization of NP content under various experimental conditions were provided. In conclusion, NPcVar is a valuable resource for understanding the relationship between species, factors, and NP contents and is anticipated to serve as a promising tool for improving the yield of high-value NPs and facilitating the development of new therapeutics.

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.

Wikipedia on the CompTox Chemicals Dashboard: Connecting Resources to Enrich Public Chemical Data

The online encyclopedia Wikipedia aggregates a large amount of data on chemistry, encompassing well over 20,000 individual Wikipedia pages and serves the general public as well as the chemistry community. Many other chemical databases and services utilize these data, and previous projects have focused on methods to index, search, and extract it for review and use. We present a comprehensive effort that combines bulk automated data extraction over tens of thousands of pages, semiautomated data extraction over hundreds of pages, and fine-grained manual extraction of individual lists and compounds of interest. We then correlate these data with the existing contents of the U.S. Environmental Protection Agency’s (EPA) Distributed Structure-Searchable Toxicity (DSSTox) database. This was performed with a number of intentions including ensuring as complete a mapping as possible between the Dashboard and Wikipedia so that relevant snippets of the article are loaded for the user to review. Conflicts between Dashboard content and Wikipedia in terms of, for example, identifiers such as chemical registry numbers, names, and InChIs and structure-based collisions such as SMILES were identified and used as the basis of curation of both DSSTox and Wikipedia. This work also allowed us to evaluate available data for sets of chemicals of interest to the Agency, such as synthetic cannabinoids, and expand the content in DSSTox as appropriate. This work also led to improved bidirectional linkage of the detailed chemistry and usage information from Wikipedia with expert-curated structure and identifier data from DSSTox for a new list of nearly 20,000 chemicals. All of this work ultimately enhances the data mappings that allow for the display of the introduction of the Wikipedia article in the community-accessible web-based EPA Comptox Chemicals Dashboard, enhancing the user experience for the thousands of users per day accessing the resource.

MediaDive: the expert-curated cultivation media database

We present MediaDive (https://mediadive.dsmz.de), a comprehensive and expert-curated cultivation media database, which comprises recipes, instructions and molecular compositions of >3200 standardized cultivation media for >40 000 microbial strains from all domains of life. MediaDive is designed to enable broad range applications from every-day-use in research and diagnostic laboratories to knowledge-driven support of new media design and artificial intelligence-driven data mining. It offers a number of intuitive search functions and comparison tools, for example to identify media for related taxonomic groups and to integrate strain-specific modifications. Besides classical PDF archiving and printing, the state-of-the-art website allows paperless use of media recipes on mobile devices for convenient wet-lab use. In addition, data can be retrieved using a RESTful web service for large-scale data analyses. An internal editor interface ensures continuous extension and curation of media by cultivation experts from the Leibniz Institute DSMZ, which is interlinked with the growing microbial collections at DSMZ. External user engagement is covered by a dedicated media builder tool. The standardized and programmatically accessible data will foster new approaches for the design of cultivation media to target the vast majority of uncultured microorganisms.