Artificial Intelligence in Drug Discovery Market by Offering, Technology, Process, Application, Therapeutic Area, End User

Description

List of Tables

The Artificial Intelligence in Drug Discovery Market was valued at USD 1.08 billion in 2023, expected to reach USD 1.35 billion in 2024, and is projected to grow at a CAGR of 27.10%, to USD 5.81 billion by 2030.

Artificial Intelligence (AI) in drug discovery is a transformative facet of pharmaceutical research and development, integrating advanced computational techniques into the molecule design and validation process. The scope of AI in this field encompasses all stages from identifying promising molecular targets to facilitating virtual screening and even predicting drug behavior in biological systems. Its necessity stems from the industry's urgent need to streamline traditional methods that are time-consuming and fraught with high failure rates. The application of AI spans various domains such as genomics research, personalized medicine, and optimization of synthetic screening processes. End-use sectors include pharmaceuticals, biotechnology firms, and contract research organizations, which leverage AI to reduce costs and improve the efficacy of drug development pipelines. Market growth is significantly influenced by technological advancements, increasing partnerships between AI firms and pharmaceutical companies, and escalating demand for precision medicine. Key opportunities arise from ongoing developments in machine learning algorithms, access to vast biomedical data, and governmental support for innovation in health technology. To seize these opportunities, companies should invest in developing robust AI platforms that enhance predictive analytics and facilitate real-time data analysis. However, challenges persist; regulatory hurdles, data privacy concerns, and the need for large, annotated datasets complicate the integration of AI in drug discovery. A well-defined innovation pathway lies in hybrid approaches that combine AI with traditional methods and interdisciplinary collaborations that advance computational biology. Future areas of interest include AI-driven drug repurposing and development of novel compound libraries. The nature of the market is highly dynamic, with rapid technological advancements and a trend towards high collaboration. Remaining agile and fostering strategic partnerships will enable stakeholders to overcome market limitations and capitalize on the increasing relevance of AI in pharmaceutical development.

KEY MARKET STATISTICS
Base Year [2023]	USD 1.08 billion
Estimated Year [2024]	USD 1.35 billion
Forecast Year [2030]	USD 5.81 billion
CAGR (%)	27.10%

Market Dynamics: Unveiling Key Market Insights in the Rapidly Evolving Artificial Intelligence in Drug Discovery Market

The Artificial Intelligence in Drug Discovery Market is undergoing transformative changes driven by a dynamic interplay of supply and demand factors. Understanding these evolving market dynamics prepares business organizations to make informed investment decisions, refine strategic decisions, and seize new opportunities. By gaining a comprehensive view of these trends, business organizations can mitigate various risks across political, geographic, technical, social, and economic domains while also gaining a clearer understanding of consumer behavior and its impact on manufacturing costs and purchasing trends.

Market Drivers
- Demand to Control Drug Discovery Process and Reduce Cost
- Increasing Need to Manage the Large Data Generated During Preclinical Studies
- Increasing Adoption across Biopharmaceutical Companies
Market Restraints
- Unavailability of Skilled Professionals
Market Opportunities
- AI Cloud to Create a Streamlined and Automated Approach in Drug Discovery
- Increasingly Growing R&D Investments
Market Challenges
- Limited Availability of Data Sets

Porter's Five Forces: A Strategic Tool for Navigating the Artificial Intelligence in Drug Discovery Market

Porter's five forces framework is a critical tool for understanding the competitive landscape of the Artificial Intelligence in Drug Discovery Market. It offers business organizations with a clear methodology for evaluating their competitive positioning and exploring strategic opportunities. This framework helps businesses assess the power dynamics within the market and determine the profitability of new ventures. With these insights, business organizations can leverage their strengths, address weaknesses, and avoid potential challenges, ensuring a more resilient market positioning.

PESTLE Analysis: Navigating External Influences in the Artificial Intelligence in Drug Discovery Market

External macro-environmental factors play a pivotal role in shaping the performance dynamics of the Artificial Intelligence in Drug Discovery Market. Political, Economic, Social, Technological, Legal, and Environmental factors analysis provides the necessary information to navigate these influences. By examining PESTLE factors, businesses can better understand potential risks and opportunities. This analysis enables business organizations to anticipate changes in regulations, consumer preferences, and economic trends, ensuring they are prepared to make proactive, forward-thinking decisions.

Market Share Analysis: Understanding the Competitive Landscape in the Artificial Intelligence in Drug Discovery Market

A detailed market share analysis in the Artificial Intelligence in Drug Discovery Market provides a comprehensive assessment of vendors' performance. Companies can identify their competitive positioning by comparing key metrics, including revenue, customer base, and growth rates. This analysis highlights market concentration, fragmentation, and trends in consolidation, offering vendors the insights required to make strategic decisions that enhance their position in an increasingly competitive landscape.

FPNV Positioning Matrix: Evaluating Vendors' Performance in the Artificial Intelligence in Drug Discovery Market

The Forefront, Pathfinder, Niche, Vital (FPNV) Positioning Matrix is a critical tool for evaluating vendors within the Artificial Intelligence in Drug Discovery Market. This matrix enables business organizations to make well-informed decisions that align with their goals by assessing vendors based on their business strategy and product satisfaction. The four quadrants provide a clear and precise segmentation of vendors, helping users identify the right partners and solutions that best fit their strategic objectives.

Strategy Analysis & Recommendation: Charting a Path to Success in the Artificial Intelligence in Drug Discovery Market

A strategic analysis of the Artificial Intelligence in Drug Discovery Market is essential for businesses looking to strengthen their global market presence. By reviewing key resources, capabilities, and performance indicators, business organizations can identify growth opportunities and work toward improvement. This approach helps businesses navigate challenges in the competitive landscape and ensures they are well-positioned to capitalize on newer opportunities and drive long-term success.

Key Company Profiles

The report delves into recent significant developments in the Artificial Intelligence in Drug Discovery Market, highlighting leading vendors and their innovative profiles. These include Aria Pharmaceuticals, Inc., Atomwise, Inc., BenevolentAI Limited, BenevolentAI SA, BioSymetrics Inc., BPGbio Inc., Butterfly Network, Inc., Cloud Pharmaceuticals, Inc., Cyclica Inc., Deargen Inc., Deep Genomics Incorporated, Envisagenics, Inc., Euretos Services BV, Exscientia PLC, Insilico Medicine, Insitro, Inc., International Business Machines Corporation, InveniAI LLC, Microsoft Corporation, Novartis AG, NVIDIA Corporation, Oracle Corporation, Owkin, Inc., Verge Genomics Inc., and XtalPi Inc..

Market Segmentation & Coverage

This research report categorizes the Artificial Intelligence in Drug Discovery Market to forecast the revenues and analyze trends in each of the following sub-markets:

Based on Offering, market is studied across Services and Software.
Based on Technology, market is studied across Context-Aware Processing, Machine Learning, and Natural Language Processing.
Based on Process, market is studied across Candidate Selection & Validation, Hit Identification & Prioritization, Hit-to-lead Identification/ Lead generation, Lead Optimization, Target Identification & Selection, and Target Validation.
Based on Application, market is studied across Biologics Design & Optimization, Disease Identification & Assessment, Safety, Toxicity, & Compliance Assessment, Small Molecule Design & Optimization, and Vaccine Design & Optimization.
Based on Therapeutic Area, market is studied across Cardiovascular Disease, Immuno-Oncology, Metabolic Diseases, and Neurodegenerative Diseases.
Based on End User, market is studied across Contract Research Organizations, Pharmaceutical & Biotechnology Companies, and Research Centers and Academic & Government Institutes.
Based on Region, market is studied across Americas, Asia-Pacific, and Europe, Middle East & Africa. The Americas is further studied across Argentina, Brazil, Canada, Mexico, and United States. The United States is further studied across California, Florida, Illinois, New York, Ohio, Pennsylvania, and Texas. The Asia-Pacific is further studied across Australia, China, India, Indonesia, Japan, Malaysia, Philippines, Singapore, South Korea, Taiwan, Thailand, and Vietnam. The Europe, Middle East & Africa is further studied across Denmark, Egypt, Finland, France, Germany, Israel, Italy, Netherlands, Nigeria, Norway, Poland, Qatar, Russia, Saudi Arabia, South Africa, Spain, Sweden, Switzerland, Turkey, United Arab Emirates, and United Kingdom.

The report offers a comprehensive analysis of the market, covering key focus areas:

1. Market Penetration: A detailed review of the current market environment, including extensive data from top industry players, evaluating their market reach and overall influence.

2. Market Development: Identifies growth opportunities in emerging markets and assesses expansion potential in established sectors, providing a strategic roadmap for future growth.

3. Market Diversification: Analyzes recent product launches, untapped geographic regions, major industry advancements, and strategic investments reshaping the market.

4. Competitive Assessment & Intelligence: Provides a thorough analysis of the competitive landscape, examining market share, business strategies, product portfolios, certifications, regulatory approvals, patent trends, and technological advancements of key players.

5. Product Development & Innovation: Highlights cutting-edge technologies, R&D activities, and product innovations expected to drive future market growth.

The report also answers critical questions to aid stakeholders in making informed decisions:

1. What is the current market size, and what is the forecasted growth?

2. Which products, segments, and regions offer the best investment opportunities?

3. What are the key technology trends and regulatory influences shaping the market?

4. How do leading vendors rank in terms of market share and competitive positioning?

5. What revenue sources and strategic opportunities drive vendors' market entry or exit strategies?

Product Code: MRR-031BF22F953D

1. Preface

1.1. Objectives of the Study
1.2. Market Segmentation & Coverage
1.3. Years Considered for the Study
1.4. Currency & Pricing
1.5. Language
1.6. Stakeholders

2. Research Methodology

2.1. Define: Research Objective
2.2. Determine: Research Design
2.3. Prepare: Research Instrument
2.4. Collect: Data Source
2.5. Analyze: Data Interpretation
2.6. Formulate: Data Verification
2.7. Publish: Research Report
2.8. Repeat: Report Update

3. Executive Summary

4. Market Overview

5. Market Insights

5.1. Market Dynamics
- 5.1.1. Drivers
  - 5.1.1.1. Demand to Control Drug Discovery Process and Reduce Cost
  - 5.1.1.2. Increasing Need to Manage the Large Data Generated During Preclinical Studies
  - 5.1.1.3. Increasing Adoption across Biopharmaceutical Companies
- 5.1.2. Restraints
  - 5.1.2.1. Unavailability of Skilled Professionals
- 5.1.3. Opportunities
  - 5.1.3.1. AI Cloud to Create a Streamlined and Automated Approach in Drug Discovery
  - 5.1.3.2. Increasingly Growing R&D Investments
- 5.1.4. Challenges
  - 5.1.4.1. Limited Availability of Data Sets
5.2. Market Segmentation Analysis
- 5.2.1. Offering: AI Software propose a revolutionary approach to drug discovery
- 5.2.2. Technology: Growing adoption of context-aware processing in personalized therapeutic
- 5.2.3. Process: Significant augmentation in the drug discovery process with computational prowess and predictive capabilities
- 5.2.4. Application: Growing usage of AI-designed small molecule drugs for human clinical trials.
- 5.2.5. Therapeutic Area: Rising adoption of AI in the drug discovery for personalized cancer treatment.
- 5.2.6. End User: Increasing use of AI in the drug discovery by pharmaceutical and biotechnology companies to accelerate their drug discovery process
5.3. Porter's Five Forces Analysis
- 5.3.1. Threat of New Entrants
- 5.3.2. Threat of Substitutes
- 5.3.3. Bargaining Power of Customers
- 5.3.4. Bargaining Power of Suppliers
- 5.3.5. Industry Rivalry
5.4. PESTLE Analysis
- 5.4.1. Political
- 5.4.2. Economic
- 5.4.3. Social
- 5.4.4. Technological
- 5.4.5. Legal
- 5.4.6. Environmental

6. Artificial Intelligence in Drug Discovery Market, by Offering

6.1. Introduction
6.2. Services
6.3. Software

7. Artificial Intelligence in Drug Discovery Market, by Technology

7.1. Introduction
7.2. Context-Aware Processing
7.3. Machine Learning
7.4. Natural Language Processing

8. Artificial Intelligence in Drug Discovery Market, by Process

8.1. Introduction
8.2. Candidate Selection & Validation
8.3. Hit Identification & Prioritization
8.4. Hit-to-lead Identification/ Lead generation
8.5. Lead Optimization
8.6. Target Identification & Selection
8.7. Target Validation

9. Artificial Intelligence in Drug Discovery Market, by Application

9.1. Introduction
9.2. Biologics Design & Optimization
9.3. Disease Identification & Assessment
9.4. Safety, Toxicity, & Compliance Assessment
9.5. Small Molecule Design & Optimization
9.6. Vaccine Design & Optimization

10. Artificial Intelligence in Drug Discovery Market, by Therapeutic Area

10.1. Introduction
10.2. Cardiovascular Disease
10.3. Immuno-Oncology
10.4. Metabolic Diseases
10.5. Neurodegenerative Diseases

11. Artificial Intelligence in Drug Discovery Market, by End User

11.1. Introduction
11.2. Contract Research Organizations
11.3. Pharmaceutical & Biotechnology Companies
11.4. Research Centers and Academic & Government Institutes

12. Americas Artificial Intelligence in Drug Discovery Market

12.1. Introduction
12.2. Argentina
12.3. Brazil
12.4. Canada
12.5. Mexico
12.6. United States

13. Asia-Pacific Artificial Intelligence in Drug Discovery Market

13.1. Introduction
13.2. Australia
13.3. China
13.4. India
13.5. Indonesia
13.6. Japan
13.7. Malaysia
13.8. Philippines
13.9. Singapore
13.10. South Korea
13.11. Taiwan
13.12. Thailand
13.13. Vietnam

14. Europe, Middle East & Africa Artificial Intelligence in Drug Discovery Market

14.1. Introduction
14.2. Denmark
14.3. Egypt
14.4. Finland
14.5. France
14.6. Germany
14.7. Israel
14.8. Italy
14.9. Netherlands
14.10. Nigeria
14.11. Norway
14.12. Poland
14.13. Qatar
14.14. Russia
14.15. Saudi Arabia
14.16. South Africa
14.17. Spain
14.18. Sweden
14.19. Switzerland
14.20. Turkey
14.21. United Arab Emirates
14.22. United Kingdom

15. Competitive Landscape

15.1. Market Share Analysis, 2023
15.2. FPNV Positioning Matrix, 2023
15.3. Competitive Scenario Analysis
- 15.3.1. Merck Enters Two Strategic Collaborations to Strengthen AI-driven Drug Discovery
- 15.3.2. Launch of Insilico's Phase II Program Highlights Generative AI Momentum
- 15.3.3. Google Cloud Launches AI-powered Solutions to Safely Accelerate Drug Discovery and Precision Medicine
15.4. Strategy Analysis & Recommendation

Companies Mentioned

1. Aria Pharmaceuticals, Inc.
2. Atomwise, Inc.
3. BenevolentAI Limited
4. BenevolentAI SA
5. BioSymetrics Inc.
6. BPGbio Inc.
7. Butterfly Network, Inc.
8. Cloud Pharmaceuticals, Inc.
9. Cyclica Inc.
10. Deargen Inc.
11. Deep Genomics Incorporated
12. Envisagenics, Inc.
13. Euretos Services BV
14. Exscientia PLC
15. Insilico Medicine
16. Insitro, Inc.
17. International Business Machines Corporation
18. InveniAI LLC
19. Microsoft Corporation
20. Novartis AG
21. NVIDIA Corporation
22. Oracle Corporation
23. Owkin, Inc.
24. Verge Genomics Inc.
25. XtalPi Inc.