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Motivation: Functional interpretation of high-throughput metabolomic and transcriptomic results is a crucial step in generating insight from experimental data. However, pathway and functional information for genes and metabolites are distributed among many siloed resources, limiting the scope of analyses that rely on a single knowledge source., Results: RaMP-DB 2.0 is a web interface, relational database, API and R package designed for straightforward and comprehensive functional interpretation of metabolomic and multi-omic data. RaMP-DB 2.0 has been upgraded with an expanded breadth and depth of functional and chemical annotations (ClassyFire, LIPID MAPS, SMILES, InChIs, etc.), with new data types related to metabolites and lipids incorporated. To streamline entity resolution across multiple source databases, we have implemented a new semi-automated process, thereby lessening the burden of harmonization and supporting more frequent updates. The associated RaMP-DB 2.0 R package now supports queries on pathways, common reactions (e.g. metabolite-enzyme relationship), chemical functional ontologies, chemical classes and chemical structures, as well as enrichment analyses on pathways (multi-omic) and chemical classes. Lastly, the RaMP-DB web interface has been completely redesigned using the Angular framework., Availability and Implementation: The code used to build all components of RaMP-DB 2.0 are freely available on GitHub at https://github.com/ncats/ramp-db, https://github.com/ncats/RaMP-Client/ and https://github.com/ncats/RaMP-Backend. The RaMP-DB web application can be accessed at https://rampdb.nih.gov/., Supplementary Information: Supplementary data are available at Bioinformatics online., (Published by Oxford University Press 2022.)

The United States has a complex regulatory scheme for marketing drugs. Understanding drug regulatory status is a daunting task that requires integrating data from many sources from the United States Food and Drug Administration (FDA), US government publications, and other processes related to drug development. At NCATS, we created Inxight Drugs (https://drugs.ncats.io), a web resource that attempts to address this challenge in a systematic manner. NCATS Inxight Drugs incorporates and unifies a wealth of data, including those supplied by the FDA and from independent public sources. The database offers a substantial amount of manually curated literature data unavailable from other sources. Currently, the database contains 125 036 product ingredients, including 2566 US approved drugs, 6242 marketed drugs, and 9684 investigational drugs. All substances are rigorously defined according to the ISO 11238 standard to comply with existing regulatory standards for unique drug substance identification. A special emphasis was placed on capturing manually curated and referenced data on treatment modalities and semantic relationships between substances. A supplementary resource 'Novel FDA Drug Approvals' features regulatory details of newly approved FDA drugs. The database is regularly updated using NCATS Stitcher data integration tool that automates data aggregation and supports full data access through a RESTful API., (Published by Oxford University Press on behalf of Nucleic Acids Research 2021.)

The Illuminating the Druggable Genome (IDG) consortium is a National Institutes of Health (NIH) Common Fund program designed to enhance our knowledge of under-studied proteins, more specifically, proteins unannotated within the three most commonly drug-targeted protein families: G-protein coupled receptors, ion channels, and protein kinases. Since 2014, the IDG Knowledge Management Center (IDG-KMC) has generated several open-access datasets and resources that jointly serve as a highly translational machine-learning-ready knowledgebase focused on human protein-coding genes and their products. The goal of the IDG-KMC is to develop comprehensive integrated knowledge for the druggable genome to illuminate the uncharacterized or poorly annotated portion of the druggable genome. The tools derived from the IDG-KMC provide either user-friendly visualizations or ways to impute the knowledge about potential targets using machine learning strategies. In the following protocols, we describe how to use each web-based tool to accelerate illumination in under-studied proteins. © 2022 The Authors. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Interacting with the Pharos user interface Basic Protocol 2: Accessing the data in Harmonizome Basic Protocol 3: The ARCHS4 resource Basic Protocol 4: Making predictions about gene function with PrismExp Basic Protocol 5: Using Geneshot to illuminate knowledge about under-studied targets Basic Protocol 6: Exploring under-studied targets with TIN-X Basic Protocol 7: Interacting with the DrugCentral user interface Basic Protocol 8: Estimating Anti-SARS-CoV-2 activities with DrugCentral REDIAL-2020 Basic Protocol 9: Drug Set Enrichment Analysis using Drugmonizome Basic Protocol 10: The Drugmonizome-ML Appyter Basic Protocol 11: The Harmonizome-ML Appyter Basic Protocol 12: GWAS target illumination with TIGA Basic Protocol 13: Prioritizing kinases for lists of proteins and phosphoproteins with KEA3 Basic Protocol 14: Converting PubMed searches to drug sets with the DrugShot Appyter., (© 2022 The Authors. Current Protocols published by Wiley Periodicals LLC.)

Background: Limited knowledge and unclear underlying biology of many rare diseases pose significant challenges to patients, clinicians, and scientists. To address these challenges, there is an urgent need to inspire and encourage scientists to propose and pursue innovative research studies that aim to uncover the genetic and molecular causes of more rare diseases and ultimately to identify effective therapeutic solutions. A clear understanding of current research efforts, knowledge/research gaps, and funding patterns as scientific evidence is crucial to systematically accelerate the pace of research discovery in rare diseases, which is an overarching goal of this study., Methods: To semantically represent NIH funding data for rare diseases and advance its use of effectively promoting rare disease research, we identified NIH funded projects for rare diseases by mapping GARD diseases to the project based on project titles; subsequently we presented and managed those identified projects in a knowledge graph using Neo4j software, hosted at NCATS, based on a pre-defined data model that captures semantics among the data. With this developed knowledge graph, we were able to perform several case studies to demonstrate scientific evidence generation for supporting rare disease research discovery., Results: Of 5001 rare diseases belonging to 32 distinct disease categories, we identified 1294 diseases that are mapped to 45,647 distinct, NIH-funded projects obtained from the NIH ExPORTER by implementing semantic annotation of project titles. To capture semantic relationships presenting amongst mapped research funding data, we defined a data model comprised of seven primary classes and corresponding object and data properties. A Neo4j knowledge graph based on this predefined data model has been developed, and we performed multiple case studies over this knowledge graph to demonstrate its use in directing and promoting rare disease research., Conclusion: We developed an integrative knowledge graph with rare disease funding data and demonstrated its use as a source from where we can effectively identify and generate scientific evidence to support rare disease research. With the success of this preliminary study, we plan to implement advanced computational approaches for analyzing more funding related data, e.g., project abstracts and PubMed article abstracts, and linking to other types of biomedical data to perform more sophisticated research gap analysis and identify opportunities for future research in rare diseases., (© 2021. The Author(s).)

Background: Humans are exposed to tens of thousands of chemical substances that need to be assessed for their potential toxicity. Acute systemic toxicity testing serves as the basis for regulatory hazard classification, labeling, and risk management. However, it is cost- and time-prohibitive to evaluate all new and existing chemicals using traditional rodent acute toxicity tests. In silico models built using existing data facilitate rapid acute toxicity predictions without using animals., Objectives: The U.S. Interagency Coordinating Committee on the Validation of Alternative Methods (ICCVAM) Acute Toxicity Workgroup organized an international collaboration to develop in silico models for predicting acute oral toxicity based on five different end points: Lethal Dose 50 ( LD 50 value, U.S. Environmental Protection Agency hazard (four) categories, Globally Harmonized System for Classification and Labeling hazard (five) categories, very toxic chemicals [ LD 50 ( LD 50 ≤ 50 mg / kg )], and nontoxic chemicals ( L D 50 > 2,000 mg / kg )., Methods: An acute oral toxicity data inventory for 11,992 chemicals was compiled, split into training and evaluation sets, and made available to 35 participating international research groups that submitted a total of 139 predictive models. Predictions that fell within the applicability domains of the submitted models were evaluated using external validation sets. These were then combined into consensus models to leverage strengths of individual approaches., Results: The resulting consensus predictions, which leverage the collective strengths of each individual model, form the Collaborative Acute Toxicity Modeling Suite (CATMoS). CATMoS demonstrated high performance in terms of accuracy and robustness when compared with in vivo results., Discussion: CATMoS is being evaluated by regulatory agencies for its utility and applicability as a potential replacement for in vivo rat acute oral toxicity studies. CATMoS predictions for more than 800,000 chemicals have been made available via the National Toxicology Program's Integrated Chemical Environment tools and data sets (ice.ntp.niehs.nih.gov). The models are also implemented in a free, standalone, open-source tool, OPERA, which allows predictions of new and untested chemicals to be made. https://doi.org/10.1289/EHP8495.

Pharos is an integrated web-based informatics platform for the analysis of data aggregated by the Illuminating the Druggable Genome (IDG) Knowledge Management Center, an NIH Common Fund initiative. The current version of Pharos (as of October 2019) spans 20,244 proteins in the human proteome, 19,880 disease and phenotype associations, and 226,829 ChEMBL compounds. This resource not only collates and analyzes data from over 60 high-quality resources to generate these types, but also uses text indexing to find less apparent connections between targets, and has recently begun to collaborate with institutions that generate data and resources. Proteins are ranked according to a knowledge-based classification system, which can help researchers to identify less studied "dark" targets that could be potentially further illuminated. This is an important process for both drug discovery and target validation, as more knowledge can accelerate target identification, and previously understudied proteins can serve as novel targets in drug discovery. Two basic protocols illustrate the levels of detail available for targets and several methods of finding targets of interest. An Alternate Protocol illustrates the difference in available knowledge between less and more studied targets. © 2020 by John Wiley & Sons, Inc. Basic Protocol 1: Search for a target and view details Alternate Protocol: Search for dark target and view details Basic Protocol 2: Filter a target list to get refined results., (© 2020 John Wiley & Sons, Inc.)

Motivation: Drug discovery investigations need to incorporate network pharmacology concepts while navigating the complex landscape of drug-target and target-target interactions. This task requires solutions that integrate high-quality biomedical data, combined with analytic and predictive workflows as well as efficient visualization. SmartGraph is an innovative platform that utilizes state-of-the-art technologies such as a Neo4j graph-database, Angular web framework, RxJS asynchronous event library and D3 visualization to accomplish these goals., Results: The SmartGraph framework integrates high quality bioactivity data and biological pathway information resulting in a knowledgebase comprised of 420,526 unique compound-target interactions defined between 271,098 unique compounds and 2018 targets. SmartGraph then performs bioactivity predictions based on the 63,783 Bemis-Murcko scaffolds extracted from these compounds. Through several use-cases, we illustrate the use of SmartGraph to generate hypotheses for elucidating mechanism-of-action, drug-repurposing and off-target prediction., Availability: https://smartgraph.ncats.io/.

Advances in the development of high-throughput screening and automated chemistry have rapidly accelerated the production of chemical and biological data, much of them freely accessible through literature aggregator services such as ChEMBL and PubChem. Here, we explore how to use this comprehensive mapping of chemical biology space to support the development of large-scale quantitative structure-activity relationship (QSAR) models. We propose a new deep learning consensus architecture (DLCA) that combines consensus and multitask deep learning approaches together to generate large-scale QSAR models. This method improves knowledge transfer across different target/assays while also integrating contributions from models based on different descriptors. The proposed approach was validated and compared with proteochemometrics, multitask deep learning, and Random Forest methods paired with various descriptors types. DLCA models demonstrated improved prediction accuracy for both regression and classification tasks. The best models together with their modeling sets are provided through publicly available web services at https://predictor.ncats.io .

The 'druggable genome' encompasses several protein families, but only a subset of targets within them have attracted significant research attention and thus have information about them publicly available. The Illuminating the Druggable Genome (IDG) program was initiated in 2014, has the goal of developing experimental techniques and a Knowledge Management Center (KMC) that would collect and organize information about protein targets from four families, representing the most common druggable targets with an emphasis on understudied proteins. Here, we describe two resources developed by the KMC: the Target Central Resource Database (TCRD) which collates many heterogeneous gene/protein datasets and Pharos (https://pharos.nih.gov), a multimodal web interface that presents the data from TCRD. We briefly describe the types and sources of data considered by the KMC and then highlight features of the Pharos interface designed to enable intuitive access to the IDG knowledgebase. The aim of Pharos is to encourage 'serendipitous browsing', whereby related, relevant information is made easily discoverable. We conclude by describing two use cases that highlight the utility of Pharos and TCRD., (Published by Oxford University Press on behalf of Nucleic Acids Research 2016. This work is written by (a) US Government employee(s) and is in the public domain in the US.)