This data package consists of bioresearch monitoring information system (BMIS) dataset, directory of the different biotech and biopharmaceutical and pharmaceutical companies in the United States and the European Union, establishment registration database, drug wholesale distributor and third-party logistics provider reporting database, establishment inspections conducted by FDA, and FDA post-marketing requirements and commitments searchable database.

This dataset provides a comprehensive, structured overview of hundreds of commonly used pharmaceutical drugs, listed alphabetically by generic name. It serves as a valuable resource for healthcare students, professionals, data analysts, and anyone interested in pharmacology.

Compiled from reputable sources like the FDA Prescribing Information, Lexicomp, and Micromedex, each entry includes detailed information on drug properties, safety, and usage. This dataset is ideal for educational purposes, data analysis projects, and as a reference for building healthcare applications.

Key Features (Columns):

Generic Name: The common name of the drug.

Drug Class: The pharmacological category (e.g., SSRI, Beta-Blocker, Statin).

Indications: The medical conditions the drug is used to treat.

Dosage Form: The physical form of the drug (e.g., Tablet, Capsule, Injection, Cream).

Strength: The potency of the drug (e.g., 500 mg, 0.1%).

Route of Administration: How the drug is administered (e.g., Oral, Topical, Intravenous).

Side Effects: Common adverse reactions associated with the drug.

Contraindications: Conditions or factors that serve as a reason to not use the drug.

Interaction warnings & Precautions: Important information on how the drug interacts with others and key safety measures.

Storage Conditions: Recommended storage instructions (e.g., Room Temperature, Refrigerate).

Reference: The primary source(s) of the information.

Availability: Whether the drug is typically available by prescription or over-the-counter (OTC).

Potential Use Cases:

Educational Tool: For students of medicine, pharmacy, and nursing to learn about drug properties.

Data Analysis & Visualization: Analyze the distribution of drug classes, common side effects, or storage requirements.

Drug Interaction Checker (Basic Foundation): Use as a base dataset to build a simple drug interaction screening tool.

Clinical Reference Application: Populate a mobile or web app with essential drug information.

Natural Language Processing (NLP): Train models to extract drug information from text or to classify drugs based on their descriptions.

File(s):

drugs_from_a_to_z.csv (The Excel data converted to a CSV for broader compatibility)

Acknowledgements:

This dataset synthesizes information from publicly available drug monographs and prescribing information from sources including the U.S. Food and Drug Administration (FDA), Lexicomp, and Micromedex.

🧪 Discover the Power of a Verified Pharma Database in IndiaIn a fast-evolving healthcare ecosystem, pharmaceutical businesses, distributors, medical marketers, and health-tech startups need access to real-time, accurate, and structured pharma data to make informed decisions and scale their operations.…

Background: In Brazil, studies that map electronic healthcare databases in order to assess their suitability for use in pharmacoepidemiologic research are lacking. We aimed to identify, catalogue, and characterize Brazilian data sources for Drug Utilization Research (DUR).Methods: The present study is part of the project entitled, “Publicly Available Data Sources for Drug Utilization Research in Latin American (LatAm) Countries.” A network of Brazilian health experts was assembled to map secondary administrative data from healthcare organizations that might provide information related to medication use. A multi-phase approach including internet search of institutional government websites, traditional bibliographic databases, and experts’ input was used for mapping the data sources. The reviewers searched, screened and selected the data sources independently; disagreements were resolved by consensus. Data sources were grouped into the following categories: 1) automated databases; 2) Electronic Medical Records (EMR); 3) national surveys or datasets; 4) adverse event reporting systems; and 5) others. Each data source was characterized by accessibility, geographic granularity, setting, type of data (aggregate or individual-level), and years of coverage. We also searched for publications related to each data source.Results: A total of 62 data sources were identified and screened; 38 met the eligibility criteria for inclusion and were fully characterized. We grouped 23 (60%) as automated databases, four (11%) as adverse event reporting systems, four (11%) as EMRs, three (8%) as national surveys or datasets, and four (11%) as other types. Eighteen (47%) were classified as publicly and conveniently accessible online; providing information at national level. Most of them offered more than 5 years of comprehensive data coverage, and presented data at both the individual and aggregated levels. No information about population coverage was found. Drug coding is not uniform; each data source has its own coding system, depending on the purpose of the data. At least one scientific publication was found for each publicly available data source.Conclusions: There are several types of data sources for DUR in Brazil, but a uniform system for drug classification and data quality evaluation does not exist. The extent of population covered by year is unknown. Our comprehensive and structured inventory reveals a need for full characterization of these data sources.

Alphabetical List of Pharmaceutical Compound or Drug Databases.

United States Imports of Pharmaceutical products was US$212.67 Billion during 2024, according to the United Nations COMTRADE database on international trade. United States Imports of Pharmaceutical products - data, historical chart and statistics - was last updated on December of 2025.

Veterinary Drug Stability Databases Market Outlook

According to our latest research, the global market size for Veterinary Drug Stability Databases reached USD 426.8 million in 2024, exhibiting robust momentum as regulatory scrutiny and innovation in veterinary pharmaceuticals intensify. The market is projected to expand at a CAGR of 8.1% from 2025 to 2033, with the forecasted market size reaching USD 852.3 million by 2033. The primary growth driver is the increasing emphasis on drug safety, efficacy, and regulatory compliance within the veterinary healthcare sector, necessitating advanced data management solutions for stability testing and documentation worldwide.

The escalating complexity of veterinary pharmaceuticals, including the rise of biologics and novel drug formulations, is a significant catalyst for the Veterinary Drug Stability Databases Market. As veterinary drug manufacturers strive to meet stringent global regulatory requirements, the demand for reliable, comprehensive, and easily accessible stability data has surged. This trend is particularly pronounced in the context of new drug development pipelines, where accurate stability profiles are essential for both regulatory submissions and post-market surveillance. The proliferation of companion animals and the intensification of livestock production further amplify the need for robust databases, ensuring consistent product quality and safety across diverse animal populations.

Another critical growth factor is the technological evolution within the pharmaceutical informatics landscape. The integration of cloud-based platforms, artificial intelligence, and advanced analytics into veterinary drug stability databases has revolutionized data management practices. These technologies facilitate real-time data capture, streamlined reporting, and predictive analytics for drug degradation, thereby enhancing the efficiency of stability testing and compliance processes. The adoption of digital solutions also enables seamless collaboration between pharmaceutical companies, contract research organizations, and regulatory authorities, fostering a more transparent and data-driven ecosystem in veterinary medicine.

Moreover, the global expansion of veterinary pharmaceutical markets, especially in emerging economies, is fueling the adoption of advanced stability database solutions. As regulatory agencies in Asia Pacific, Latin America, and the Middle East & Africa strengthen their oversight of veterinary drugs, local manufacturers and multinational corporations alike are investing in sophisticated data infrastructures to ensure global market access. This trend is further supported by increased funding for animal health research and a growing awareness of the importance of drug stability in preventing adverse events and ensuring therapeutic efficacy over the product lifecycle.

Regionally, North America continues to dominate the Veterinary Drug Stability Databases Market, driven by a mature pharmaceutical industry, high regulatory standards, and early adoption of digital health technologies. However, Asia Pacific is emerging as the fastest-growing region, propelled by rapid industrialization of animal agriculture, expanding veterinary healthcare infrastructure, and rising investments in pharmaceutical R&D. Europe maintains a strong presence due to its robust regulatory environment and focus on animal welfare, while Latin America and the Middle East & Africa are witnessing steady growth as local industries modernize and integrate global best practices.

Database Type Analysis

The Veterinary Drug Stability Databases Market is segmented by database type into Chemical Stability Databases, Microbiological Stability Databases, Physical Stability Databases, and Others. Chemical Stability Databases hold the largest market share, reflecting the critical importance of monitoring chemical degradation pathways, shelf-life, and potency for both small molecule drugs and biologics. These databases provide detailed records of active pharmaceutical ingredient (API) integrity under various environmental conditions, supporting regulatory submissions and quality assurance protocols. The growing complexity of veterinary drug formulations, including combination therapies and extended-release products, has intensified the demand for comprehensive chemical stability tracking, further cementing the dominance of this segment.

The VA Drug Pricing database contains the current prices for pharmaceuticals purchased by the federal government. These listed prices are based on the Federal Supply Schedule (FSS). This database is mandated by Public Law 102-585, the Veterans Health Care Act of 1992, which sets the maximum amount that a drug may be bought for by the Veterans Health Administration (VHA). The source of this information is contained in printed contracts or data files supplied by the drug manufacturers, representing the pricing agreements between VHA and the manufacturers. Price data is input by the National Acquisition Center (NAC) into the database administered by the Pharmacy Benefits Management Strategic Health Care Group. Information from this database is published on the World Wide Web at the following site: http://www.pbm.va.gov. The users of this database include pharmaceutical manufacturers, drug wholesalers, Office of Inspector General (OIG) and those who purchase pharmaceuticals for the VHA and other government agencies.

The database provides information on prescribed amounts, levels detected in aquatic environments, chemical structure, molecular weight, octanol-water partition coefficients, water solubility, environmental persistence, general toxicity information and specific toxicity levels to five groups of organisms (algae, mollusks, finfish, crustaceans, and select terrestrial animals).

The global Drug Reference App market is poised for substantial expansion, projected to reach a market size of approximately $1,800 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 16.5% anticipated through 2033. This significant growth is propelled by a confluence of factors, primarily the escalating need for accurate, up-to-date pharmaceutical information among healthcare professionals, researchers, and students. The increasing prevalence of chronic diseases necessitates continuous access to comprehensive drug databases for effective patient management and treatment. Furthermore, the proliferation of smartphones and the widespread adoption of digital health solutions are creating fertile ground for the adoption of these essential applications. Key drivers include the demand for enhanced clinical decision support, streamlined drug discovery and development processes, and the growing emphasis on evidence-based medicine. The market is segmented into distinct applications, with Doctors representing the largest segment due to their direct involvement in prescribing and managing medications, followed by Researchers, Students, and Other users. The landscape of the Drug Reference App market is characterized by several prevailing trends and a few inherent restraints. A significant trend is the integration of advanced features such as artificial intelligence (AI) and machine learning (ML) to provide personalized drug recommendations, identify potential drug interactions, and predict treatment outcomes. The development of user-friendly interfaces, offline access capabilities, and multilingual support are also crucial for broadening accessibility and enhancing user experience. The rise of specialized drug reference apps catering to specific therapeutic areas or professional niches is another notable trend. However, challenges such as data security and privacy concerns, the cost of maintaining extensive and updated drug databases, and the need for continuous regulatory compliance can act as restraints. Despite these hurdles, the market is expected to witness strong growth driven by continuous innovation and the indispensable role these apps play in modern healthcare. Key players like Epocrates, Wolters Kluwer (Lexicomp), and Medscape are at the forefront, continually evolving their offerings to meet the dynamic needs of the healthcare ecosystem. This comprehensive report delves into the dynamic Drug Reference App market, providing in-depth analysis and actionable insights for stakeholders. Covering a study period from 2019 to 2033, with a base year of 2025 and a forecast period extending from 2025 to 2033, the report meticulously examines historical trends and future projections. The estimated market size for 2025 is projected to reach $3.5 million, with significant growth anticipated throughout the forecast period.

This data package contains UK controlled drugs database, US food prices database, US nationwide food consumption survey, US national health and nutrition examination survey, US healthy eating index and data on food affordability for households led by females.

Note: AED, Adverse Effects Database; BMI, Body Mass Index; BN, Bureau of Nutrition Department of Health; BNCD, Bureau of Non-Communicable Disease; BP, Blood Pressure; BPS, Bureau of Planning and Strategy; BRFSS, Behavioral Risk Factors Surveillance System; CA, Cancer; CBC, Complete Blood count; CGRN, ConvergenceCT Global Research Network; COPD, Chronic Obstructive Pulmonary Disease; CR, Cancer Registry; DM, Diabetes Mellitus; DMH, Department of Mental Health; EBMP, Medical Data Vision EBM Provider®;EKG, Electrocardiography; ESRD, End-Stage Renal Disease; FBS, Fasting Blood Sugar; FSR, The Fukuoka Stroke Registry; HAT, The Heart Association of Thailand under the Royal Patronage; Hb, Hemoglobin;HbA1c, Hemoglobin A1c;Hct, Hematocrit; HDL, High-density Lipoprotein; HFCT, Heart Failure Council of Thailand; HIV/AIDS, Human Immunodeficiency Virus/Acquired Immunodeficiency Syndrome; HPVC, Health Product vigilance Center Thai FDA;HSRI, Health System Research Institute; HTN, Hypertension; HWS, Health and Welfare Survey; IHD, Ischemic Heart Disease; IMS NPA, IMS NPA data; JARM DB, Rehabilitation Patients Database; JARM, the Japanese Association of Rehabilitation Medicine; JDPC, The Japanese Diagnosis Procedure Combination database; JMDC, Japan Medical Data Center Claims Database; JMIRI, JMIRI Pharmacy Claims DB; JPIC, Japan Pharmaceutical Information Center; JSTAR, Japanese Study of Aging and Retirement; LDL, Low-density Lipoprotein; Mdevice, Database of Medical Device; MEDIS-DC, Medical Information System Development Center; MHLW, List of Statistical Surveys conducted by MHLW;MHLW, Ministry of Health, Labor and Welfare; MICS, Multiple Indicator Cluster Survey; NCI, National Cancer Institute; NCPCDB, NIHON CHOUZAI Pharmacy Claims DB; NDS, National Disability Survey; NESMH, National Epidemiology Survey on Mental Health; NHES, National Health Examination Survey; NHSO, National Health Security Office; NHWS, National Health and Wellness Survey; NNS, National Nutrition Survey; NSO, National Statistical Office; NST, Nephrology Society of Thailand; OU, Osaka University; PCI, Percutaneous Coronary Intervention; PCU, Primary care unit; PHC, Population and Housing Census; PMDA, Pharmaceutical and Medical Devices Agency; QOL, Quality of Life; RAD-AR, Risk/benefit Assessment of Drug-Analysis & Response; RCPT, The Royal College of Physiatrists of Thailand; RHS, Reproductive Health Survey; RIETI, The Research Institute of Economy, Trade and Industry; SES, Socio-Economic Survey; TC, Total Cholesterol; TDR, Thai Diabetes Registry; TES, Thailand Endocrinology Society; TG, Triglyceride; Thai ADHERE, Thai Acute Decompensated Heart Failure Registry; TPCIR, Thai National Percutaneous Coronary Intervention Registry; TPDR, Thai Parkinson’s Disease Registry; TRC, Thai Red Cross Society; TRRTR, Thailand Renal Replacement Therapy Registry; TSRR, Thai Stroke Rehabilitation RegistryCharacteristics of databases.

Techsalerator covers all healthcare professionals and contacts with emails, NPI addresses, home addresses and more.

This dataset includes all types of Healthcare professional categories including:

Abdominal Radiology Addiction Medicine Addiction Psychiatry, Psychiatry Adolescent Medicine, Pediatrics Adolescent Medicine, Internal Medicine Adult Cardiac Anesthesiology, Anesthesiology Adult Congenital Heart Disease, Internal Medicine Adult Reconstructive Orthopaedic Surgery, Orthopaedic Surgery Advanced Heart Failure and Transplant Cardiology, Internal Medicine Aerospace Medicine, Preventive Medicine Allergy and Immunology Anesthesiology Anesthesiology Critical Care Medicine, Emergency Medicine Blood Banking-Transfusion Medicine, Pathology Brain Injury Medicine, Neurology Brain Injury Medicine, Physical Medicine and Rehabilitation Brain Injury Medicine, Psychiatry Cardiothoracic Radiology, Diagnostic Radiology Cardiovascular Disease, Internal Medicine Chemical Pathology, Pathology Child Abuse Pediatrics, Pediatrics Child and Adolescent Psychiatry, Psychiatry Child Neurology/Pediatric Neurology, Neurology Clinical Biochemical Genetics, Medical Genetics and Genomics Clinical Cardiac Electrophysiology, Internal Medicine Clinical Genetics and Genomics, Medical Genetics and Genomics Clinical Informatics, Diagnostic Radiology Clinical Informatics, Anesthesiology Clinical Informatics, Preventive Medicine Clinical Informatics, Pathology Clinical Neurophysiology, Neurology Colon and Rectal Surgery Complex Family Planning, Obstetrics and Gynecology Complex General Surgical Oncology, General Surgery Complex Pediatric Otolaryngology, Otolaryngology-Head and Neck Surgery Congenital Cardiac Surgery, Thoracic Surgery/Thoracic and Cardiac Surgery Consultation-Liaison Psychiatry/Psychosomatic Medicine, Psychiatry Craniofacial Surgery, Plastic Surgery Critical Care Medicine, Obstetrics and Gynecology Critical Care Medicine, Anesthesiology Critical Care Medicine, Internal Medicine Cytopathology, Pathology Dermatology Dermatopathology, Pathology Dermatopathology, Dermatology Developmental and Behavioral Pediatrics, Pediatrics Diagnostic Medical Physics, Medical Physics Diagnostic Radiology Emergency Medical Services, Emergency Medicine Emergency Medicine Endocrinology, Diabetes, and Metabolism, Internal Medicine Endovascular Surgical Neuroradiology, Neurological Surgery Endovascular Surgical Neuroradiology, Diagnostic Radiology Epilepsy, Neurology Family Medicine/Family Practice Female Pelvic Medicine and Reconstructive Surgery, Obstetrics and Gynecology Female Pelvic Medicine and Reconstructive Surgery, Urology Foot and Ankle Orthopaedic Surgery, Orthopaedic Surgery Forensic Pathology, Pathology Forensic Psychiatry, Psychiatry Gastroenterology, Internal Medicine General Surgery Geriatric Medicine, Family Medicine/Family Practice Geriatric Medicine, Internal Medicine Geriatric Psychiatry, Psychiatry Gynecologic Oncology, Obstetrics and Gynecology Hand Surgery, General Surgery Hand Surgery, Orthopaedic Surgery Hand Surgery, Plastic Surgery Hematology, Internal Medicine Hematology and Medical Oncology, Internal Medicine Hematopathology/Hematology, Pathology Hospice and Palliative Medicine Infectious Disease, Internal Medicine Integrated Plastic Surgery Integrated Thoracic Surgery Integrated Vascular Surgery Internal Medicine Internal Medicine-Critical Care Medicine, Emergency Medicine Internal Medicine-Emergency Medicine Internal Medicine-Pediatrics Internal Medicine-Psychiatry Interventional Cardiology, Internal Medicine Interventional Radiology, Diagnostic Radiology Laboratory Genetics and Genomics, Medical Genetics and Genomics Maternal-Fetal Medicine, Obstetrics and Gynecology Medical Biochemical Genetics, Medical Genetics and Genomics Medical Genetics and Genomics Medical Microbiology Pathology, Pathology Medical Oncology, Internal Medicine Medical Physics, Diagnostic Radiology Medical Toxicology, Pediatrics Medical Toxicology, Emergency Medicine Medical Toxicology, Preventive Medicine Micrographic Dermatologic Surgery, Dermatology Molecular Genetic Pathology, Pathology Molecular Genetic Pathology, Medical Genetics and Genomics Musculoskeletal Imaging Radiology, Diagnostic Radiology Musculoskeletal Oncology Musculoskeletal Oncology, Orthopaedic Surgery Neonatal-Perinatal Medicine, Pediatrics Nephrology, Internal Medicine Neuro-Ophthalmology, Ophthalmology Neurocritcial Care , Anesthesiology Neurocritical Care, Internal Medicine Neurocritical Care, Neurological Surgery Neurocritical Care, Neurology Neurodevelopmental Disabilities, Neurology Neurological Surgery Neurology Neuromuscular Medicine, Neurology Neuromuscular Medicine, Physical Medicine and Rehabilitation Neuropathology, Pathology Neuroradiology, Diagnostic Radiology Neurotology, Otolaryngology-Head and Neck Surgery Nuclear Medical Physics, Med...

Saudi Arabia Drug Database (Metadata, PILs & SPCs) - AR/EN

🟢 Overview

This is a comprehensive database of registered pharmaceutical products in the Kingdom of Saudi Arabia, collected from the official public portal of the Saudi Food and Drug Authority (SFDA).

This dataset is uniquely bilingual (Arabic / English) and provides rich, structured metadata (JSON). This makes it a valuable resource for researchers, students, Natural Language Processing (NLP) specialists, and data scientists interested in the healthcare and pharmaceutical informatics sectors in the Middle East.

🔑 Key Features

• Rich Metadata: Each drug includes detailed structured data (see "Data Structure" below), such as official price, trade and generic names, legal classification, manufacturer, agent, and storage conditions.
• Bilingual (AR/EN): Provides the "Patient Information Leaflet" (PIL) in both Arabic and English, opening significant opportunities for bilingual NLP research.
• Specialized Leaflets (SPCs): Contains the "Summary of Product Characteristics" (SPC), the technical leaflet aimed at healthcare professionals, which provides in-depth technical data.
• Processor-Ready Format (JSON): The data is organized in a JSON format, making it easy to parse and process programmatically.
• Comprehensive: The vast majority of drug records contain the full set of metadata and all three associated leaflets.

🗂️ Data Structure

The dataset is provided as a single .zip archive which contains 563 individual JSON files.

• Each JSON file contains a list of 15 drug records.
• Each drug record is an object containing its metadata and the three leaflet texts.

Example Single Drug Record

Each drug record contains a Drug Data object (the metadata) and three keys for the leaflets:

json{
 "Drug Data": {
  "Registration Number": "0202256789",
  "Register Year": "2025",
  "Trade Name": "Brevie",
  "Generic Name": "BRIVARACETAM",
  "Strength": "50",
  "Strength Unit": "mg",
  "Administration Route": "Oral use",
  "Pharmaceutical Form": "Film-coated tablet",
  "Package Size": "60",
  "Packages Types": "Blister",
  "Legal Classification": "Prescription",
  "Product Control": "Uncontrolled",
  "Drug Type": "Generic",
  "ShelfLife in Months": "36",
  "Storage Conditions": "do not store above 30°c",
  "Public price (SAR)": "266.05",
  "Manufacture": "MSN LABORATORIES PRIVATE LIMITED",
  "الوكيل": "SUDAIR PHARMA COMPANY",
  "Marketing Company": "SUDAIR PHARMA COMPANY"
 },
 "Patient Information Leaflet (PIL) in English": "[...English leaflet text...]",
 "Patient Information Leaflet (PIL) in Arabic": "[...Arabic leaflet text...]",
 "Summary of Product Characteristics (SPC)": "[...Healthcare professional leaflet text...]"
}
````
## 🔗 Data Collection Code

The full code used to collect and structure this dataset is publicly available on GitHub:

👉 **[Data Collection Repository](https://github.com/MQushaym/web-scraping-data-collection)**

This repository contains the web scraping and data processing scripts used to compile and clean the dataset.


-----

## 🎯 Potential Use Cases

 * **AI Agents & RAG (Retrieval-Augmented Generation):**

   * **(Highly Recommended)** Building a specialized AI Agent (like a GPT or LLM assistant) that answers complex questions about Saudi-registered drugs.
   * This dataset acts as a perfect "Knowledge Base" for RAG. The agent can retrieve specific leaflets (PILs/SPCs) or structured metadata (like price, storage, manufacturer) to provide accurate, verifiable, and context-aware answers.
   * Developing advanced Q\&A systems for both patients ("Can I take this drug with X?") and professionals ("What are the contraindications for this drug?").

 * **Natural Language Processing (NLP):**

   * Building specialized medical terminology translation models (Ar/En).
   * Named Entity Recognition (NER) to identify side effects, active ingredients, and dosages from the leaflet texts.
   * Text summarization of the long SPC and PIL documents.

 * **Data Analysis & Health Informatics:**

   * Analyzing drug pricing in relation to manufacturers or drug type (Generic/Innovator).
   * Constructing knowledge graphs (KGs) that link drugs, ingredients, manufacturers, and legal classifications.
   * Studying storage conditions in relation to pharmaceutical forms.

-----

## 📄 License & Citation

This dataset is made available under the **CC BY-NC 4.0 (Attribution-NonCommercial 4.0)** license.

This means you are free to use it for **academic and research purposes** as long as you provide **attribution (citation)** and do not use it for commercial purposes.

When using this dataset, please cite as follows:

> **Data collected and structured by:** Meshal AL-Qushaym
> **Dataset:** KS...

2107412 Global exporters importers export import shipment records of Pharmaceutical with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

231824 Global exporters importers export import shipment records of Pharma with prices, volume & current Buyer's suppliers relationships based on actual Global export trade database.

The NCI DIS 3D database is a collection of 3D structures for over 400,000 drugs. The database is an extension of the NCI Drug Information System. The structural information stored in the DIS is only the connection table for each drug. The connection table is just a list of which atoms are connected and how they are connected. It is essentially a searcheable database of three-dimensional structures has been developed from the chemistry database of the NCI Drug Information System (DIS), a file of about 450,000 primarily organic compounds which have been tested by NCI for anticancer activity. The DIS database is very similar in size and content to the proprietary databases used in the pharmaceutical industry; its development began in the 1950s; and this history led to a number of problems in the generation of 3D structures. This information can be searched to find drugs that share similar patterns of connections, which can correlate with similar biological activity. But the cellular targets for drug action, as well as the drugs themselves, are 3 dimensional objects and advances in computer hardware and software have reached the point where they can be represented as such. In many cases the important points of interaction between a drug and its target can be represented by a 3D arrangement of a small number of atoms. Such a group of atoms is called a pharmacophore. The pharmacophore can be used to search 3D databases and drugs that match the pharmacophore could have similar biological activity, but have very different patterns of atomic connections. Having a diverse set of lead compounds increases the chances of finding an active compound with acceptable properties for clinical development. Sponsor: The ICBG are supported by the Cooperative Agreement mechanism, with funds from nine components of the NIH, the National Science Foundation, and the Foreign Agricultural Service of the USDA.

These supplementary materials represent a dataset in the form of a quantitative database of antibiotics' developers, and a partial collection of semi-structured interviews, collected and analyzed in the research article "Beyond resistance: How can alternative innovation models develop and enhance global access to new antibiotics?". This article is one of the outcomes of the "New Business Models for Pharmaceutical Innovation and Global Access to Medicines" research project, conducted at the Global Health Center, within the Geneva Graduate Institute. The dataset contains a quantitative dataset of antibiotics' developers and their characteristics, as well as 6/11 interviews collected and used in this article, which are published with the informed consent of the interviewees.

IntroductionInteractions between pharmaceutical companies and healthcare providers are increasingly scrutinized by academics, professionals, media, and politicians. Most empirical studies and professional guidelines focus on unilateral donor-recipient types of interaction and overlook, or fail to distinguish between, more reciprocal types of interaction. However, the degree of goal alignment and potential for value creation differs in these two types of interactions. Failing to differentiate between these two forms of interaction between pharmaceutical companies and healthcare providers could thus lead to biased conclusions regarding their desirability. This study reviews the empirical literature regarding the effects of bilateral forms of interactions between pharmaceutical companies and healthcare providers in order to explore their effects.Material and methodsWe searched two medical databases (i.e. PubMed and Cochrane Library) and one business database (i.e. EBSCO) for empirical, peer-reviewed articles concerning any type of bilateral interaction between pharmaceutical companies and healthcare providers. We included quantitative articles which were written in English and published between January 1st, 2000 and October 31st, 2016, and where the title or abstract included a combination of synonyms of the following keywords: pharmaceutical companies, healthcare providers, interaction, and effects.ResultsOur search results yielded 10 studies which were included in our analysis. These studies focused on either research-oriented interaction or on education-oriented interaction. The included studies reported various outcomes of interaction such as prescribing behavior, ethical dilemmas, and research output. Regardless of the type of interaction, the studies either reported no significant effects or ambivalent outcomes such as affected clinical practice or ethical issues.Discussion and conclusionThe effects of bilateral interactions reported in the literature are similar to those reported in studies concerning unilateral interactions. The theoretical notion that bilateral interactions between pharmaceutical companies and healthcare providers have different effects given their increased level of goal alignment thus does not seem to hold. However, most of the empirical studies focus on intermediary, provider-level, outcomes such as altered prescribing behavior. Outcomes at the health system level such as overall costs and quality of care are overlooked. Further research is necessary in order to disentangle various forms of value created by different types of interactions between pharmaceutical companies and healthcare providers.