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 the analyst’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 the analyst’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.
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 the analyst’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 the analyst’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.
Table of Contents
- Players
- Thematic Briefing
- Trends
- Industry Analysis
- Value Chain
- Companies
- Drug Development Scorecard
- Abbreviations
- Further Reading
- About the Authors
- Thematic Research Methodology
Table 1: Healthcare trends
Table 2: The key technology trends impacting AI in drug discovery
Table 3: The key macroeconomic trends impacting AI in drug discovery
Table 4: The key regulatory trends impacting AI in drug discovery
Table 5: Drugs in clinical development and includes information
Table 6: Strategic alliances
Table 7: Top VC deals associated with AI in drug discovery announced from June 2022 to August 2024
Table 8: M&A
Table 9: Target identification and validation
Table 10: The time taken to identify novel drug targets.
Table 11: The different platforms and libraries used for drug repurposing
Table 12: The leading technology players within the AI in drug discovery theme and summarizes their competitive position
Table 13: The specialist AI vendors in drug discovery and summarizes their competitive position
Table 14: The leading adopters of AI in drug discovery and summarizes their competitive position
Table 15: Abbreviations
Table 16: the analyst reports
List of Figures
Figure 1: Examples of leading players in AI in drug discovery and place in the value chain
Figure 2: The five categories of advanced AI capabilitiesp
Figure 3: Global AI platform, hardware, and consulting and support services revenue in pharma, 2019-28
Figure 4: Top companies by number of drugs developed using AI-based technologies
Figure 5: Breakdown of drugs by therapy area
Figure 6: Pharma companies’ confidence level in AI within the pharma industry
Figure 7: AI can benefit different aspects of the pharmaceutical value chain.
Figure 8: Top Influencer trends related to AI
Figure 9: Top influencer posts related to AI and drug discovery, 2024
Figure 10: AI in drug discovery value chain
Figure 11: Examples of leaders and challengers in target identification and validation
Figure 12: The use of AI in clinical trials
Figure 13: Examples of leaders and challengers in target identification and validation
Figure 14: Examples of leaders and challengers in drug repurposing
Figure 15: Who does what in the drug development space?
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
Figure 17: Our valuation screen ranks our universe of companies within a sector based on selected valuation metrics
Figure 18: Our risk screen ranks companies within a particular sector based on overall investment risk
Companies Mentioned (Partial List)
A selection of companies mentioned in this report includes, but is not limited to:
- Alphabet
- Amazon
- Dassault Systèmes
- IBM
- Cisco
- Informatica
- Oracle
- Microsoft
- BenevolentAI
- Exscientia
- Recursion
- IQVIA
- Insilico Medicine
- Syneos Health
- Parexel
- AstraZeneca
- Boehringer Ingelheim
- GSK
- Eli Lilly and Co
- Novartis
- Pfizer
- Roche
- Sanofi
- Takeda
- Merck & Co
