In recent years, the pharma industry has been taken over by a wave of digital transformation, leading to the integration of advanced technologies across different aspects of the pharma value chain.
Artificial intelligence (AI) and big data are at the forefront of driving innovation, from enhancing drug discovery to optimizing clinical trial design.
The drug discovery process is a very expensive and time-consuming process. Despite recent technological advancements, the success rate of research and development (R&D) is very low, adding emphasis to the need for innovative technologies, such as AI, to improve efficiency and outcomes.
This report consolidates GlobalData’s latest thinking and forecasts around how the healthcare, macroeconomic, technology, and regulatory trends will impact the AI in drug discovery, as well as providing insights into the leading players and future disruptors across the value chain, and providing insights into key drugs and markets from GlobalData’s Pharma Intelligence Center. Additionally, this report is designed to provide strategic planners, competitive intelligence professionals and key stakeholders in the pharmaceutical industry a clear view of the opportunities and risks over the foreseeable future for AI.
Scope
A dedicated report examining the pivotal healthcare, technological, macroeconomic, and regulatory trends shaping the AI-driven drug discovery landscape. This report also provides an in-depth analysis of how these trends are poised to either accelerate progress or create obstacles for the growth of the AI in drug discovery market.
Reasons to Buy
Understand the key trends accelerating or hindering the AI in drug discovery space.
See market forecasts for different therapies within AI up to 2028.
Understand recent and influential developments in AI.
Review of leaders and disruptors across the AI value chain.
Executive Summary
Players
Table Figure 1: Examples of leading players in AI in drug discovery and place in the value chain
Thematic Briefing
What is AI?
Advanced AI capabilities
Table Figure 2: The five categories of advanced AI capabilitiesp
AI in drug discovery
Trends
Healthcare trends
Table Healthcare trends
Technology trends
Table The key technology trends impacting AI in drug discovery
Macroeconomic trends
Table The key macroeconomic trends impacting AI in drug discovery
Regulatory trends
Table The key regulatory trends impacting AI in drug discovery
Industry Analysis
Market size and growth forecasts
Table Figure 3: Global AI platform, hardware, and consulting and support services revenue in pharma, 2019-28
Analysis of drugs discovered using AI
Table Figure 4: Top companies by number of drugs developed using AI-based technologies
Table Figure 5: Breakdown of drugs by therapy area
Table Drugs in clinical development and includes information
Survey data on the adoption of AI in pharma
Table Figure 6: Pharma companies' confidence level in AI within the pharma industry
Table Figure 7: AI can benefit different aspects of the pharmaceutical value chain.
AI in Drug Discovery key opinion leader research
Challenges associated with traditional drug discovery
How AI is used to address the challenges of drug discovery
Limitations of the use of AI in drug discovery
The role of AI over the next five to 10 years
Current AI-driven drug discovery market trend
The most critical stage of AI in drug discovery
Key Lessons from working with AI in drug discovery.
Deals
Strategic alliances
Table Strategic alliances
Funding
Table Top VC deals associated with AI in drug discovery announced from June 2022 to August 2024
M&A
Table M&A
Case Studies
Exscientia and AWS Provide an Innovative Approach to Drug Discovery
Predictive Oncology Employs Innovative 3D Cell Technology to Develop Cancer Therapies
Eurofins Discovery's New AI-Driven Platform Accurately Predicts ADMET Properties of Molecules
1910 Genetics Collaborates with Microsoft to Bring Change to Drug Discovery
Dyno Therapeutics to Use Nvidia's BioNeMo to Improve Drug Discovery of Gene Therapies
ImmunitoAI's AI Platform to Enhance Antibody-Based Drug Discovery
Standigm Partner with Institut Pasteur Korea to Develop a New Drug for Resistant Tuberculosis
Selection of a New Heart Failure Target Shows Continued Success Between BenevolentAI and AstraZeneca
Social media trends
Table Figure 8: Top Influencer trends related to AI
Table Figure 9: Top influencer posts related to AI and drug discovery, 2024
Value Chain
Table Figure 10: AI in drug discovery value chain
Target identification and validation
Table Target identification and validation
Table Figure 11: Examples of leaders and challengers in target identification and validation
Table The time taken to identify novel drug targets.
Clinical trials
Table Figure 12: The use of AI in clinical trials
Table Figure 13: Examples of leaders and challengers in target identification and validation
Drug repurposing
Table Figure 14: Examples of leaders and challengers in drug repurposing
Table The different platforms and libraries used for drug repurposing
Companies
Leading AI technology vendors
Table The leading technology players within the AI in drug discovery theme and summarizes their competitive position
Specialist AI vendors in drug discovery
Table The specialist AI vendors in drug discovery and summarizes their competitive position
Leading pharma adopters of AI in drug discovery
Table The leading adopters of AI in drug discovery and summarizes their competitive position
Sector Scorecards
Drug development sector scorecard
Who's who
Table Figure 15: Who does what in the drug development space?
Thematic screen
Table Figure 16: Our thematic screen ranks companies based on overall leadership in the 10 themes that matter most to their industry, generating a leading indicator of future performance
Valuation screen
Table Figure 17: Our valuation screen ranks our universe of companies within a sector based on selected valuation metrics
Risk screen
Table Figure 18: Our risk screen ranks companies within a particular sector based on overall investment risk