About

The DrugBank database is a unique bioinformatics and cheminformatics resource that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information. The database contains nearly 4800 drug entries including >1,480 FDA-approved small molecule drugs, 128 FDA-approved biotech (protein/peptide) drugs, 71 nutraceuticals and >3,200 experimental drugs. Additionally, more than 2,500 non-redundant protein (i.e. drug target) sequences are linked to these FDA approved drug entries. Each DrugCard entry contains more than 100 data fields with half of the information being devoted to drug/chemical data and the other half devoted to drug target or protein data.

Openness

Not open due to noncommercial conditions of re-use (from about page):

DrugBank is offered to the public as a freely available resource. Use and re-distribution of the data, in whole or in part, for commercial purposes requires explicit permission of the authors and explicit acknowledgment of the source material (DrugBank) and the original publication (see below). We ask that users who download significant portions of the database cite the DrugBank paper in any resulting publications.

This dataset Druggable Genome Comprehensive Drug Targets is a selection of supplementary data from "The Druggable Genome: Evaluation of Drug Targets in Clinical Trials Suggests Major Shifts in Molecular Class and Indication" (2013) [PMID:24016212]. The comprehensive list includes 461 targets of approved drugs.

DrugBank Vocabulary contains information on DrugBank identifiers, names, and synonyms to permit easy linking and integration into any type of project. DrugBank is a richly annotated resource that combines detailed drug data with comprehensive drug target and drug action information. DrugBank is widely used to facilitate in silico drug target discovery, drug design, drug docking or screening, drug metabolism prediction, drug interaction prediction and general pharmaceutical education.

Database of bibliographic details of over 9,000 references published between 1951 and the present day, and includes abstracts, journal articles, book chapters and books replacing the two former separate websites for Ian Stolerman's drug discrimination database and Dick Meisch's drug self-administration database. Lists of standardized keywords are used to index the citations. Most of the keywords are generic drug names but they also include methodological terms, species studied and drug classes. This index makes it possible to selectively retrieve references according to the drugs used as the training stimuli, drugs used as test stimuli, drugs used as pretreatments, species, etc. by entering your own terms or by using our comprehensive lists of search terms. Drug Discrimination Drug Discrimination is widely recognized as one of the major methods for studying the behavioral and neuropharmacological effects of drugs and plays an important role in drug discovery and investigations of drug abuse. In Drug Discrimination studies, effects of drugs serve as discriminative stimuli that indicate how reinforcers (e.g. food pellets) can be obtained. For example, animals can be trained to press one of two levers to obtain food after receiving injections of a drug, and to press the other lever to obtain food after injections of the vehicle. After the discrimination has been learned, the animal starts pressing the appropriate lever according to whether it has received the training drug or vehicle; accuracy is very good in most experiments (90 or more correct). Discriminative stimulus effects of drugs are readily distinguished from the effects of food alone by collecting data in brief test sessions where responses are not differentially reinforced. Thus, trained subjects can be used to determine whether test substances are identified as like or unlike the drug used for training. Drug Self-administration Drug Self-administration methodology is central to the experimental analysis of drug abuse and dependence (addiction). It constitutes a key technique in numerous investigations of drug intake and its neurobiological basis and has even been described by some as the gold standard among methods in the area. Self-administration occurs when, after a behavioral act or chain of acts, a feedback loop results in the introduction of a drug or drugs into a human or infra-human subject. The drug is usually conceptualized as serving the role of a positive reinforcer within a framework of operant conditioning. For example, animals can be given the opportunity to press a lever to obtain an infusion of a drug through a chronically-indwelling venous catheter. If the available dose of the drug serves as a positive reinforcer then the rate of lever-pressing will increase and a sustained pattern of responding at a high rate may develop. Reinforcing effects of drugs are distinguishable from other actions such as increases in general activity by means of one or more control procedures. Trained subjects can be used to investigate the behavioral and neuropharmacological basis of drug-taking and drug-seeking behaviors and the reinstatement of these behaviors in subjects with a previous history of drug intake (relapse models). Other applications include evaluating novel compounds for liability to produce abuse and dependence and for their value in the treatment of drug dependence and addiction. The bibliography is updated about four times per year.

Bioinformatics and cheminformatics database that combines detailed drug (i.e. chemical, pharmacological and pharmaceutical) data with comprehensive drug target (i.e. sequence, structure, and pathway) information.

KEGG DRUG is a comprehensive drug information resource for approved drugs in Japan, USA, and Europe unified based on the chemical structures and/or the chemical components, and associated with target, metabolizing enzyme, and other molecular interaction network information.

Psychedelic Drug Database

Psychotropic and psychedelics drugs database with molecular descriptors

By Juan Jose [source]

About this dataset

This dataset is a comprehensive database of psychotropic and psychedelic drugs, focusing on their molecular descriptors. The data was sourced from the PubChem Database, which is a widely-used resource for chemical information. The main objective of this project is to create an easily accessible and centralized database specifically for psychedelic compounds.

To achieve this, the dataset includes information on identified psychedelic compounds obtained from the PubChem Database. Additionally, molecular descriptors for these compounds were generated using the KNIME Analytics Platform and RDKit module. These molecular descriptors provide important characteristics and properties of each compound, making it easier to perform quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) analyses.

By providing access to such data, researchers and scientists can have a valuable resource for studying psychoactive substances in a more efficient manner. This database offers consolidated and accurate information about various psychotropic drugs, aiding in research related to their effects, mechanisms of action, toxicity profiles, and potential therapeutic uses.

External resources used in this project include the PubChem Project website as well as the KNIME Analytics Platform and RDKit software tools. With these resources combined, this dataset serves as a dependable repository for both basic research purposes as well as applications in drug design or development efforts targeting psychoactive substances.

The columns within this dataset provide detailed information about each compound's molecular descriptors derived from its chemical structure. This diverse set of characteristics enables researchers to compare different compounds based on their structural features or predict certain properties using computational models.

Overall, this comprehensive psychotropic and psychedelics drugs database plays a crucial role in advancing understanding of these substances' pharmacological activities while facilitating more efficient drug discovery processes through predictive modeling approaches like QSAR/QSPR analysis

How to use the dataset

Understanding the Columns

• Compound Name: The name or identifier of each compound in the database.
• Molecular Formula: The chemical formula representing the number and types of atoms in a compound.
• Molecular Weight: The mass of a molecule, calculated as the sum of atomic weights.
• Canonical SMILES: A simplified molecular representation using standardised notation for atoms and bonds.
• Isomeric SMILES: A more specific molecular representation that includes information about stereochemistry (the spatial arrangement of atoms). 6-10. Additional columns may be included with specific molecular descriptors depending on how they were generated.

Accessing Additional Information

To delve deeper into any given compound in this database, make use of external resources such as The PubChem Project. This comprehensive resource provides additional data on each compound including chemical properties, biological activities, safety information, and much more.

Performing QSAR or QSPR Analysis

One potential application for this dataset is Quantitative Structure-Activity Relationship (QSAR) or Quantitative Structure-Property Relationship (QSPR) analysis. These approaches involve studying the relationship between a set of chemical properties (molecular descriptors) and an observed activity/property value for a set of compounds.

To perform QSAR/QSPR analysis using this dataset:

• Import these data into your preferred analytics platform such as KNIME Analytics Platform.
• Use the molecular descriptors provided in the dataset as independent variables.
• Obtain an activity/property dataset as your dependent variable (e.g., biological activity, toxicity, physical property).
• Apply appropriate machine learning or statistical modeling techniques to build a model that predicts the activity/property based on the molecular descriptors.
• Evaluate and validate your model using suitable methods (e.g., cross-validation, external test set).

Precautions and Ethical Considerations

While this database provides valuable information for research purposes, it is essential to handle psychedelic substances with caution and adhere to legal and ethical considerations.

• Leg...

Database of authoritative health information about diseases, conditions, and wellness issues that offers reliable, up-to-date health information for free. It contains the latest treatments, information on drugs and supplements, the meanings of words, and medical videos and illustrations. Links to the latest topic or disease specific medical research or clinical trials are also offered. * MedlinePlus pages contain carefully selected links to Web resources with health information on over 900 topics. ** The MedlinePlus health topic pages include links to current news on the topic and related information. You can also find preformulated searches of the MEDLINE/PubMed database, which allow you to find references to latest health professional articles on your topic. * The A.D.A.M. medical encyclopedia brings health consumers an extensive library of medical images and videos, as well as over 4,000 articles about diseases, tests, symptoms, injuries, and surgeries. * The Merriam-Webster medical dictionary allows you to look up definitions and spellings of medical words. * Drug and supplement information is available from the American Society of Health-System Pharmacists (ASHP) via AHFS Consumer Medication Information, and Natural Medicines Comprehensive Database Consumer Version. ** AHFS Consumer Medication Information provides extensive information about more than 1,000 brand name and generic prescription and over-the-counter drugs, including side effects, precautions and storage for each drug. ** Natural Medicines Comprehensive Database Consumer Version is an evidence-based collection of information on alternative treatments. MedlinePlus has 100 monographs on herbs and supplements. * Interactive tutorials from the Patient Education Institute explain over 165 procedures and conditions in easy-to-read language. An XML File for the MedlinePlus Health Topics is available, http://www.nlm.nih.gov/medlineplus/xmldescription.html. The ontology is available through Bioportal, http://bioportal.bioontology.org/ontologies/MEDLINEPLUS

The Drug Product Database (DPD) system captures information on Canadian human, veterinary and disinfectant products approved for use by Health Canada. To facilitate the use of the drug product data, multiple Drug Product files are available. Users can access the complete data set through the “Drug Product” file. Subsets of the data can be accessed in the “Drug Product By …” files. The data in these files are filtered based on the current drug product status. For example, only drug product data for Approved products will be found in the “Drug Product By Approved Status” file.

Drug-target interactions constitute the fundamental basis for understanding drug action mechanisms and advancing therapeutic discovery. While existing drug-target databases have contributed valuable resources, they exhibit structural and functional fragmentation due to heterogeneous data sources and annotation standards. Building upon the high-confidence drug-gene interactions curated in HCDT 1.0, we present HCDT 2.0, a comprehensive and standardized resource that expands the scope through multiomics data integration. This update incorporates three-dimensional interactions including drug-gene, drug-RNA and drug-pathway interactions. The current version contains 1,284,353 curated interactions: 1,224,774 drug-gene pairs (678,564 drugs × 5,692 genes), 11,770 drug-RNA mappings (316 drugs × 6,430 RNAs), and 47,809 drug-pathway links (6,290 drugs ×3,143 pathways), alongside 16,317 drug-disease associations. To enhance biological interpretability, we further integrated pathway-gene and RNA-gene regulatory relationships. In addition, we integrated 38,653 negative DTIs covering 26,989 drugs and 1,575 genes. This integrative framework not only addresses critical gaps in cross-scale data representation but also establishes a robust foundation for systems pharmacology applications, including drug repurposing, adverse event prediction, and precision oncology strategies.

This page links to the data associated with the publication "ToxicoDB: an integrated database to mine and visualize large-scale toxicogenomic datasets ". The data have been curated and analyzed using our open-source R package, ToxicoGx (https://github.com/bhklab/ToxicoGx) and are available publicly in the ToxicoDB web application (www.toxicodb.ca). Please see the included DOIs below, or download the .csv file which contains the names, dates and DOIs of all datasets listed here.

The TGGATES data was generated by Igarashi Y, Nakatsu N, Yamashita T, Ono A, Ohno Y, Urushidani T, Yamada H. Open TG-GATEs: a large-scale toxicogenomics database. Nucleic Acids Res [Internet]. 2015 Jan;43(Database issue):D921–7. Available from: http://dx.doi.org/10.1093/nar/gku955 PMCID: PMC4384023.

Data:

TGGATEs humanldh (https://doi.org/10.5281/zenodo.3762812)

TGGATEs humandna (https://doi.org/10.5281/zenodo.4024859)

TGGATEs ratldh (https://doi.org/10.5281/zenodo.3762817)

TGGATEs ratdna (https://doi.org/10.5281/zenodo.4024918)

This Drug Matrix data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413.

Data:

Drug Matrix (https://doi.org/10.5281/zenodo.3766569)

Drugs Interaction Checker Market Outlook

The global Drugs Interaction Checker market size was valued at approximately $1.8 billion in 2023 and is projected to reach around $3.5 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.8% during the forecast period. The increasing focus on patient safety, the growing prevalence of chronic diseases, and the rising awareness about the importance of preventing adverse drug interactions are some of the key factors driving market growth.

One of the major growth drivers for this market is the increasing emphasis on patient safety and the prevention of adverse drug reactions (ADRs). ADRs are a significant cause of morbidity and mortality worldwide, and healthcare systems are continuously seeking ways to mitigate these risks. Drugs interaction checkers play a crucial role in identifying potential drug-drug interactions, thus preventing harmful effects and enhancing patient safety. Furthermore, the integration of advanced technologies like artificial intelligence (AI) and machine learning (ML) in drug interaction software has significantly improved their accuracy and effectiveness, further propelling market growth.

Another key factor contributing to the market expansion is the growing prevalence of polypharmacy, particularly among the elderly population. Polypharmacy refers to the use of multiple medications by a patient, which is common in older adults due to the presence of multiple chronic conditions. This increases the risk of drug interactions, making it essential for healthcare providers to use drug interaction checker tools to manage and monitor medication regimens effectively. Additionally, the rising awareness among patients about the importance of checking drug interactions also fuels the demand for these tools.

The increasing adoption of electronic health records (EHRs) and other digital health solutions is also boosting the market. EHRs often include integrated drug interaction checkers, enabling healthcare professionals to instantly check for potential interactions as they prescribe medications. This seamless integration not only improves efficiency but also significantly reduces the risk of prescription errors. Moreover, the trend towards personalized medicine, which requires detailed patient-specific information, further underscores the need for advanced drug interaction checkers.

Regionally, North America currently holds the largest share of the drug interaction checker market, primarily due to the well-established healthcare infrastructure, high adoption rate of advanced healthcare technologies, and supportive government initiatives. However, the Asia Pacific region is expected to exhibit the highest growth rate during the forecast period, driven by the increasing healthcare expenditure, growing awareness about patient safety, and the rapid adoption of digital health solutions.

Component Analysis

The Drugs Interaction Checker market can be segmented by component into software and services. The software segment dominates the market and is expected to continue its lead during the forecast period. This includes standalone software and integrated solutions within EHR systems. The increasing demand for real-time, accurate drug interaction data is a significant growth driver for this segment. Additionally, software solutions are continuously evolving with the incorporation of advanced technologies like AI and ML, which enhance their predictive capabilities and user-friendliness.

Services, on the other hand, encompass a range of offerings including consulting, implementation, and ongoing support. As healthcare providers increasingly adopt drug interaction checkers, the need for proper implementation and staff training becomes critical. This drives the demand for related services. Moreover, continuous updates and support services are essential to ensure that the software remains effective and up-to-date with the latest drug information. This segment is witnessing steady growth as healthcare institutions recognize the importance of comprehensive support services in maximizing the benefits of drug interaction checkers.

The integration of drug interaction checkers with existing healthcare systems is often complex, requiring specialized knowledge and expertise. This has led to an increasing reliance on professional services to ensure successful integration. Furthermore, with the growing emphasis on data security and compliance with healthcare regulations, the role of services in ensuring that drug interaction checkers meet these standards has become more prominent.

Additional file 8. SQL script to store data from CSV DrugBank file (ADMET, molecular descriptors) to IDAAPM.

Our human medicines product database is a comprehensive and up-to-date listing of all medicines that have been assessed by the HPRA and granted a licence so that they can be marketed in Ireland. The list includes all prescription and over-the-counter medicines whether they are original brand-name medicines or generic versions. The summary of product characteristics document (known as SPC) is also provided for each medicine. The SPC is typically used by healthcare professionals. The package leaflet which includes information for patients and members of the public is also available for a number of medicines. All medicines centrally authorised by the European Medicines Agency are also listed.

This is a complete list of all Covance drug development locations along with their geographic coordinates. Covance, Inc. (NYSE: CVD), formerly Corning Incorporated, with headquarters in Princeton, New Jersey, USA, was a contract research organization (CRO), providing drug development and animal testing services. It provided the world's largest central laboratory network, and employed a global team of clinical-trial professionals and cardiac-safety experts. It became a publicly traded company after being spun off by Corning, Inc., in 1997. After being acquired and rebranded by LabCorp, this list is now closed.

This data was generated by Ganter B, Snyder RD, Halbert DN, Lee MD. Toxicogenomics in drug discovery and development: mechanistic analysis of compound/class-dependent effects using the DrugMatrix database. Pharmacogenomics [Internet]. 2006 Oct;7(7):1025–1044. Available from: http://dx.doi.org/10.2217/14622416.7.7.1025 PMID: 17054413. The data have been curated and analyzed using our open-source R package, ToxicoGx (cran.r-project.org/web/packages/ToxicoGx) and are available publicly in the ToxicoDB web application (www.toxicodb.ca).

This is a complete list of all Bartell Drugs locations, along with their geographic coordinates. Bartell Drugs is a chain of pharmacies with locations in the Puget Sound area in Washington state. Bartell Drugs was started in 1890 by George H. Bartell. Bartell Drugs is the oldest family owned drugstore chain in the United States.

The Drug Listing Act of 1972 requires registered drug establishments to provide the Food and Drug Administration (FDA) with a current list of all drugs manufactured, prepared, propagated, compounded, or processed by it for commercial distribution. (See Section 510 of the Federal Food, Drug, and Cosmetic Act (Act) (21 U.S.C. � 360)). Drug products are identified and reported using a unique, three-segment number, called the National Drug Code (NDC), which serves as a universal product identifier for drugs. FDA publishes the listed NDC numbers and the information submitted as part of the listing information in the NDC Directory which is updated daily.

NCI''s comprehensive cancer database that contains summaries on a wide range of cancer topics; a registry of 8,000+ open and 19,000+ closed cancer clinical trials from around the world; a directory of professionals who provide genetics services; the NCI Dictionary of Cancer Terms, with definitions for 6,800+ cancer and medical terms; and the NCI Drug Dictionary, which has information on 2,300+ agents used in the treatment of cancer or cancer-related conditions. The PDQ cancer information summaries are peer reviewed and updated monthly by six editorial boards comprised of specialists in adult treatment, pediatric treatment, supportive care, screening and prevention, genetics, and complementary and alternative medicine. The Boards review current literature from more than 70 biomedical journals, evaluate its relevance, and synthesize it into clear summaries. Many of the summaries are also available in Spanish.

This is a complete list of all Kinney Drugs Locations, along with their geographic coordinates. Kinney Drugs is a chain of drug stores and pharmacies throughout Central and Northern New York, as well as Vermont. Its headquarters is located in Gouverneur.