This statistic shows the total number of drugs in the R&D pipeline worldwide from 2001 to 2025. In 2001, there were ***** drugs in the R&D pipeline, whereas there were ****** drugs in the pipeline in January 2025.

One of the key requirements for incorporating machine learning (ML) into the drug discovery process is complete traceability and reproducibility of the model building and evaluation process. With this in mind, we have developed an end-to-end modular and extensible software pipeline for building and sharing ML models that predict key pharma-relevant parameters. The ATOM Modeling PipeLine, or AMPL, extends the functionality of the open source library DeepChem and supports an array of ML and molecular featurization tools. We have benchmarked AMPL on a large collection of pharmaceutical data sets covering a wide range of parameters. Our key findings indicate that traditional molecular fingerprints underperform other feature representation methods. We also find that data set size correlates directly with prediction performance, which points to the need to expand public data sets. Uncertainty quantification can help predict model error, but correlation with error varies considerably between data sets and model types. Our findings point to the need for an extensible pipeline that can be shared to make model building more widely accessible and reproducible. This software is open source and available at: https://github.com/ATOMconsortium/AMPL.

In 2022, U.S.-based company Tempus was the world's leading data processor for drug development based on investments. Investments for the company stood at *** billion U.S. dollars that year. Tempus uses advanced data analytics and artificial intelligence/machine learning for drug discovery and development.

A database providing information about compounds under investigation for therapeutic use. There are currently 44 compounds on record with 100 compounds in the process of being uploaded to the database.

This data package contains information on approved, researched and proven drug targets and drug lists.

Success.ai’s Pharma Data for the Global Pharmaceutical Industry provides a robust dataset tailored for businesses looking to connect with pharmaceutical companies, decision-makers, and key stakeholders worldwide. Covering pharmaceutical manufacturers, research organizations, biotech firms, and distributors, this dataset offers verified SIC codes, firmographic details, and contact information for executives and operational leads.

With access to over 170 million verified professional profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and business development strategies are driven by reliable, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is essential for navigating the competitive global pharmaceutical landscape.

Why Choose Success.ai’s Pharma Data?

1. Verified Contact Data for Precision Outreach

   • Access verified work emails, phone numbers, and LinkedIn profiles of pharmaceutical executives, R&D leads, compliance officers, and procurement managers.
   • AI-driven validation ensures 99% accuracy, optimizing your campaigns and improving communication efficiency.

2. Comprehensive Coverage of the Global Pharmaceutical Sector

   • Includes profiles of pharmaceutical companies, biotech firms, contract manufacturing organizations (CMOs), and distributors across North America, Europe, Asia, and other major markets.
   • Gain insights into regional pharmaceutical trends, product pipelines, and market dynamics unique to global markets.

3. Continuously Updated Datasets

   • Real-time updates reflect leadership changes, mergers, product launches, and regulatory compliance shifts.
   • Stay aligned with the fast-paced pharmaceutical industry to capitalize on emerging opportunities and maintain relevance.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 170M+ Verified Professional Profiles: Engage with decision-makers, R&D specialists, and operational leaders in the pharmaceutical industry worldwide.
• 30M Company Profiles: Access detailed firmographic data, including company sizes, revenue ranges, and geographic footprints.
• Verified SIC Codes: Understand industry classifications and product specializations to refine your targeting strategies.
• Leadership Contact Details: Connect with CEOs, COOs, medical directors, and regulatory managers influencing pharmaceutical operations.

Key Features of the Dataset:

1. Decision-Maker Profiles in Pharmaceuticals

   • Identify and engage with professionals overseeing R&D, clinical trials, supply chains, and regulatory compliance.
   • Target leaders responsible for drug development, vendor selection, and market entry strategies.

2. Advanced Filters for Precision Targeting

   • Filter companies by industry segment (biotech, generic pharmaceuticals, vaccines), geographic location, or revenue size.
   • Tailor campaigns to align with specific needs such as drug pipeline acceleration, production scaling, or market expansion.

3. SIC Codes and Firmographic Insights

   • Access verified SIC codes and detailed company profiles to understand market focus, operational scale, and specialization areas.
   • Use firmographic data to prioritize high-value targets and align product offerings with market demands.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data allow for personalized messaging, highlight unique value propositions, and improve engagement outcomes with pharmaceutical stakeholders.

Strategic Use Cases:

1. Sales and Lead Generation

   • Offer technology solutions, research tools, or contract services to pharmaceutical manufacturers, R&D facilities, and distribution networks.
   • Build relationships with procurement teams and compliance officers responsible for vendor approvals and operational excellence.

2. Market Research and Product Development

   • Analyze global pharmaceutical trends, drug approval patterns, and regulatory frameworks to guide product innovation and market entry strategies.
   • Identify high-growth regions and emerging therapeutic areas to focus your resources effectively.

3. Partnership and Supply Chain Development

   • Connect with pharmaceutical companies seeking contract manufacturing, raw material sourcing, or distribution partnerships.
   • Foster alliances that streamline production, ensure quality, and accelerate time-to-market.

4. Regulatory Compliance and Risk Mitigation

   • Engage with regulatory officers and compliance managers overseeing adherence to local and international pharmaceutical standards.
   • Present solutions for documentation, reporting, and risk management to ensure compliance and operational efficiency.

Why Choose Success.ai?

1. Best Price Guarantee
   ...

100 random FORESEE pipelines were chosen and then trained on all 266 available drugs in the GDSC database to predict the patient data sets. (XLSX)

The Database is a research and analysis tool developed at the University of Washington, in the Department of Pharmaceutics. It contains in vitro and in vivo information on drug interactions in humans from the following sources: * 9648 peer-reviewed journal articles referenced in PubMed * 102 New Drug Applications (NDAs) * 411 excerpts of FDA Prescribing Information * In-depth analyses of drug-drug interactions in the context of 40 diseases / co-morbidities. In addition, the database also provides PK Profiles of drugs, QT Prolongation data, including results of TQT studies from recent NDAs, as well as Regulatory Guidances and Editorial Summaries/Syntheses relevant to advances in the field of drug interactions. Access to the Database is licensed by UW Center for Commercialization (C4C) to organizations interested in in-depth information on drug interactions. The Database is particularly useful to scientists/clinicians working in drug discovery and drug development. Database users can search for information using several families of pre-formulated queries based on drug name, enzyme name, transporter name, therapeutic area, and more.

Drug Delivery Infusion Systems are used to administer medication to a patient in critical care, emergency care, home care and trauma centers. GlobalData's Medical Devices sector report, “Drug Delivery Infusion Systems – Medical Devices Pipeline Assessment, 2016' provides comprehensive information about the Drug Delivery Infusion Systems pipeline products with comparative analysis of the products at various stages of development and information about the clinical trials which are in progress. The Drug Delivery Infusion Systems Pipeline Assessment report provides key information and data related to: Extensive coverage of the Drug Delivery Infusion Systems under development Review details of major pipeline products which include product description, licensing and collaboration details and other developmental activities including pipeline territories, regulatory paths and estimated approval dates Reviews of major players involved in the pipeline product development. Provides key clinical trial data related to ongoing clinical trials such as trial phase, trial status, trial start and end dates, and, the number of trials of the major Drug Delivery Infusion Systems pipeline products. Review of Recent Developments in the segment / industry The Drug Delivery Infusion Systems Pipeline Assessment report enables you to: Access significant competitor information, analysis, and insights to improve your R&D strategies Identify emerging players with potentially strong product portfolio and create effective counter-strategies to gain competitive advantage Identify and understand important and diverse types of Drug Delivery Infusion Systems under development Formulate market-entry and market expansion strategies Plan mergers and acquisitions effectively by identifying major players with the most promising pipeline The major companies covered in the “Drug Delivery Infusion Systems – Medical Devices Pipeline Assessment, 2016” report: 410 Medical Innovation, LLC Acuros GmbH AktiVax Inc. Alnylam Pharmaceuticals, Inc. Anesthesia Safety Products, LLC AngioDynamics, Inc. Automedics Medical Systems Baxter International Inc. BioCardia, Inc. Debiotech S.A. Delpor, Inc. Edwards Lifesciences Corporation Eksigent Technologies, LLC Eli Lilly and Company Flowonix Medical, Inc. FluidSynchrony, LLC Fluonic, Inc. Fresenius Kidney Care Imagnus Biomedical Inc. Innovfusion Pte. Ltd. Intarcia Therapeutics, Inc. IRadimed Corporation Ivenix, Inc. LifeMedix, LLC Lynntech, Inc. Medallion Therapeutics, Inc. Medical Device Creations Ltd. MedPrime Technologies Pvt. Ltd. Mercator MedSystems, Inc. Nano Precision Medical NexGen Medical Systems, Inc. Nipro Corporation Owen Mumford Limited Pavmed Inc PRO-IV Medical Ltd. Ratio, Inc. Rice University SteadyMed Therapeutics, Inc. StnDrd Infusion Corporation Tel Aviv University Terumo Corporation ToucheMedical Ltd. Unilife Corporation University of Minnesota University of Southern California Note: Certain sections in the report may be removed or altered based on the availability and relevance of data in relation to the equipment type. The GlobalData Differentiation This report is prepared using data sourced from in-house databases, secondary and primary research by GlobalData's team of industry experts. The data and analysis within this report are driven by GlobalData Medical Equipment (GDME) databases. GlobalData Medical Equipment database gives you comprehensive information required to drive sales, investment and deal-making activity in your business. It includes the following: 15,000+ data tables showing market size across more than 780 medical equipment segments and 15 countries, from 2007 and forecast to 2021 10,000+ primary interviews, conducted annually to ensure data and report quality 1,100+ medical equipment conference reports 2,000+ industry-leading reports per annum, covering growing sectors, market trends, investment opportunities and competitive landscape 600+ analysis reports, covering market and pipeline product analysis, by indication; medical equipment trends and issues, and investment and M&A trends 56,500+ medical equipment company profiles 4,100+ company profiles of medical equipment manufacturers in China and India 2,000+ company profiles of medical equipment manufacturers in Japan 825+ companies’ revenue splits and market shares 1,750+ quarterly and annual medical equipment company financials 700+ medical equipment company SWOTs 19,000+ pipeline product profiles 27,000+ marketed product profiles 33,000+ clinical trials 25,000+ trial investigators 20,600+ product patents 3,700+ reports on companies with products under development 21,500+ reports on deals in the medical equipment industry 1,300+ surgical and diagnostic procedures by therapy area 50+ key healthcare indicators by country For more information or to receive a free demonstration of the service, please visit: GlobalData Medical Custom Requirements Contact us to discuss the areas of your business where you need external input, and we will work with you to identify the strongest way forward to meet your needs. Read More

This dataset contains a CSV file of clinical trial data used to develop an interactive visualization published by Aero Data Lab, titled “A Bird’s Eye View of Research Landscape.” The data offers insights into pharmaceutical research and development trends and serves as a valuable resource for exploring the structure and scope of clinical trials. Published in 2019, the dataset can support studies in medical innovation, trial phases, and therapeutic focus areas.

Drug Labels and Side Effects Dataset

Dataset Overview

This comprehensive pharmaceutical synthetic dataset contains 1,393 records of synthetic drug information with 15 columns, designed for data science projects focusing on healthcare analytics, drug safety analysis, and pharmaceutical research. The dataset simulates real-world pharmaceutical data with appropriate variety and realistic constraints for machine learning applications.

Dataset Specifications

Column Specifications

Categorical Features

Numerical Features

*Intentionally stored as mixed types for data cleaning practice

Key Statistics

Manufacturer Distribution

Drug Class Distribution

Side Effect Severity

Potential Use Cases

1. Machine Learning Applications

• Drug Approval Prediction: Predict approval likelihood based on drug characteristics
• Price Prediction: Estimate drug pricing using features like class, manufacturer, dosage
• Side Effect Classification: Classify severity based on drug properties
• Market Success Analysis: Analyze factors contributing to drug market performance

2. Data Engineering Projects

• ETL Pipeline Development: Practice data cleaning and transformation
• Data Quality Assessment: Implement data validation and quality checks
• Database Design: Create normalized pharmaceutical database schema
• Real-time Processing: Stream processing for drug monitoring systems

3. Business Intelligence

• Pharmaceutical Market Analysis: Manufacturer market share and competitive analysis
• Drug Safety Analytics: Side effect patterns and safety profile analysis
• Regulatory Compliance: Approval trends and regulatory timeline analysis
• Pricing Strategy: Competitive pricing analysis across drug classes

Recommended Next Steps

1. Data Cleaning Pipeline: Implement comprehe...

Success.ai’s Prospect Data for Biotechnology & Pharmaceutical Innovators Globally provides a powerful dataset designed to connect businesses with key players driving innovation in the biotech and pharmaceutical industries worldwide. Covering companies engaged in drug development, biotechnology research, and life sciences innovation, this dataset offers verified profiles, professional histories, work emails, and phone numbers of decision-makers and industry leaders.

With access to over 700 million verified global profiles and 30 million company profiles, Success.ai ensures your outreach, market research, and partnership efforts are powered by accurate, continuously updated, and AI-validated data. Supported by our Best Price Guarantee, this solution is indispensable for navigating the fast-evolving biotech and pharmaceutical landscape.

Why Choose Success.ai’s Prospect Data for Biotech and Pharmaceutical Innovators?

1. Verified Contact Data for Industry Professionals

   • Access verified work emails, phone numbers, and LinkedIn profiles of executives, R&D leads, compliance officers, and procurement managers in the biotech and pharmaceutical sectors.
   • AI-driven validation ensures 99% accuracy, reducing bounce rates and maximizing communication efficiency.

2. Comprehensive Coverage Across Global Markets

   • Includes profiles of professionals and companies from North America, Europe, Asia-Pacific, and other emerging biotech and pharmaceutical markets.
   • Gain insights into global industry trends, drug development pipelines, and regional innovations.

3. Continuously Updated Datasets

   • Real-time updates capture leadership changes, research breakthroughs, funding activities, and regulatory compliance updates.
   • Stay ahead of market developments and align your strategies with industry dynamics.

4. Ethical and Compliant

   • Adheres to GDPR, CCPA, and other global privacy regulations, ensuring responsible use of data and compliance with legal standards.

Data Highlights:

• 700M+ Verified Global Profiles: Engage with decision-makers, researchers, and executives in biotech and pharmaceutical industries worldwide.
• 30M Company Profiles: Access detailed firmographic data, including revenue ranges, research capacities, and operational footprints.
• Professional Histories: Gain insights into the expertise, career progressions, and roles of professionals driving innovation.
• Leadership Contact Information: Connect directly with CEOs, R&D heads, regulatory managers, and other key stakeholders shaping the future of life sciences.

Key Features of the Dataset:

1. Decision-Maker Profiles in Biotech and Pharmaceuticals

   • Identify and engage with professionals leading research, clinical trials, supply chains, and compliance efforts.
   • Target individuals responsible for strategic decisions in drug development, technology integration, and regulatory adherence.

2. Advanced Filters for Precision Targeting

   • Filter professionals and companies by industry focus (biotech, generics, vaccines), geographic region, revenue size, or workforce composition.
   • Tailor campaigns to address specific needs, such as drug discovery, manufacturing scalability, or market entry.

3. Research and Innovation Insights

   • Access data on research priorities, product pipelines, and innovation trends across global biotech and pharmaceutical sectors.
   • Leverage these insights to position your offerings effectively and uncover new opportunities.

4. AI-Driven Enrichment

   • Profiles enriched with actionable data enable personalized messaging, highlight unique value propositions, and improve engagement outcomes with industry professionals.

Strategic Use Cases:

1. Sales and Lead Generation

   • Present products, services, or technologies that accelerate R&D, streamline production, or ensure compliance to biotech and pharmaceutical companies.
   • Build relationships with procurement teams, regulatory managers, and R&D heads managing budgets and resource allocation.

2. Market Research and Competitive Analysis

   • Analyze global trends in biotechnology and pharmaceuticals to guide product innovation and strategic planning.
   • Benchmark against competitors to identify market gaps, emerging niches, and high-growth opportunities.

3. Partnership Development and Licensing

   • Engage with organizations seeking strategic partnerships, co-development opportunities, or licensing agreements for drug development.
   • Foster alliances that drive mutual growth and innovation in life sciences.

4. Regulatory Compliance and Risk Mitigation

   • Connect with compliance officers and legal professionals overseeing regulatory adherence, clinical trials, and product approvals.
   • Offer solutions that simplify compliance reporting, risk management, and quality assurance processes.

Why Choose Success.ai?

1. Best Price...

Global Big Data in Drug Development Market is segmented by Application (Pharmaceutical R&D_Clinical Trials_Regulatory Submissions_Personalized Medicine_Market Access), Type (Predictive Analytics_Patient Data Analytics_Clinical Trial Analytics_Real-World Data Integration_AI-Powered Drug Development), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

This dataset contains information regarding the development of targeted GC-MS methods for forensic seized drug analysis. Included in this dataset are method parameter files, mass spectra, mass spectral databases, and retention time / retention index data.

PHMSA regulations in 49 CFR Part 199 require operators of pipelines, liquefied natural gas plants, and underground natural gas storage facilities to submit data to the DOT DAMIS. Operators with less than 50 covered employees are required to submit once every three years, not annually.

The Petroleum Product Pipelines dataset was updated on May 10, 2021 from the Energy Information Administration (EIA), with attribute data from the end of calendar year 2024 and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). Major petroleum product pipelines in the United States. Layer includes interstate trunk lines and selected intrastate lines. Based on publicly available data from a variety of sources with varying scales and levels of accuracy. Updated January 2020. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529022

This data was created for the purpose of identifying major petroleum product pipelines in the United States. Major petroleum product pipelines in the United States. Layer includes interstate trunk lines and selected intrastate lines. Based on publicly available data from a variety of sources with varying scales and levels of accuracy. Updated January 2020.

The success of artificial intelligence (AI) models has been limited by the requirement of large amounts of high-quality training data, which is just the opposite of the situation in most drug discovery pipelines. Active learning (AL) is a subfield of AI that focuses on algorithms that select the data they need to improve their models. Here, we propose a two-phase AL pipeline and apply it to the prediction of drug oral plasma exposure. In phase I, the AL-based model demonstrated a remarkable capability to sample informative data from a noisy data set, which used only 30% of the training data to yield a prediction capability with an accuracy of 0.856 on an independent test set. In phase II, the AL-based model explored a large diverse chemical space (855K samples) for experimental testing and feedback. Improved accuracy and new highly confident predictions (50K samples) were observed, which suggest that the model’s applicability domain has been significantly expanded.

Since 2016, under Guide BB of the Filing Manual, major CER-regulated pipeline companies are required to report their pipelines’ throughput and capacity data at key points every quarter. Oil and liquids pipelines report monthly data and natural gas pipelines report daily data. This dataset can be explored in interactive dashboard format: A look at pipeline flow and capacity. Throughput and capacity graphs can also be explored by individual pipeline in Pipeline Profiles. The dataset covers 2006 to present. Files are updated quarterly but may be refreshed as needed. Trans Mountain product categorizations changed in May 2024. The data before this change is archived in “ARCHIVED Trans Mountain Pipeline”. The current data set (“Trans Mountain Pipeline”) contains new and updated product categorizations.

Discover the burgeoning Batten disease drug pipeline market. This analysis reveals key trends, leading companies (Abeona, Amicus, RegenxBio), projected market growth (15% CAGR), and regional market shares. Learn about challenges and opportunities in developing treatments for this rare, devastating disease.