The Artificial Intelligence (AI) in Drug Discovery Market Size was valued at USD 1.42 billion in 2023 and is expected to reach USD 11.37 billion by 2031, and grow at a CAGR of 29.7% over the forecast period 2024-2031.
The adoption of AI solutions in the clinical trial process reduces possible obstacles, shortens cycle times and increases productivity and accuracy when conducting clinical trials. Stakeholders in the life sciences industry are thus becoming increasingly enthusiastic about adopting these advanced artificial intelligence solutions for drug discovery processes.
The discovery and development of drugs is a costly process that takes up an appreciable amount of time. The average cost of the discovery and development of new treatments is 2.6 billion US dollars, with a period of over 10 years, according to data reported by industry journals. The majority of candidate therapies are eliminated during the early stages of clinical trials, particularly in preclinical and phase-1 trials, due to the narrow scope of development testing. This directly leads to the substantial costs and extended timelines associated with the process.
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DRIVERS
Precision medicine improves the efficacy of treatment
Precision medicine, using artificial intelligence to discover new drugs, is more effective in treating patients by defining the therapeutic approach according to each patient's particular characteristics. AI analyses vast data sources including Genetic, Molecular and Clinical Information with a view to identifying specific patients' profiles and predicting the best drug responses. This personalised approach is contributing to the growing use of artificial intelligence in drug discovery worldwide, improving treatment outcomes and minimising adverse reactions. The ability to focus on treatments is in line with the overall objective of creating better and more efficient medicines.
RESTRAINTS
Lack of standardized protocols on different AI platforms and tools
This variability hinders the seamless integration and cooperation between different technologies, which is a barrier to interoperability. Data sharing, consistency and compatibility across different artificial intelligence applications in the discovery of drugs are complicated by the absence of widely accepted protocols. This lack of standardisation poses a problem for effective communication and delivery processes, which could limit the scale or reach of AI solutions in this sector.
OPPORTUNITIES
Increasing Strategic Initiatives Fosters Development of AI-Driven Solutions
There has been a surge in collaboration, partnerships and investments as pharmaceutical companies and research institutes increasingly recognize the potential of artificial intelligence. The development of innovative AI driven solutions and the creation of a collaborative ecosystem to accelerate the discovery of drugs is encouraged by these strategic initiatives. Such partnerships may lead to the pooling of resources, expertise, and diverse data sets, thereby increasing the efficiency of AI algorithms in identifying potential drug candidates and optimising the drug development process.
CHALLENGES
Ethical concerns arising from algorithmic bias
The increasing contribution of artificial intelligence systems to decision processes, biases embedded in algorithms may unintentionally exacerbate disparities between healthcare outcomes. In the context of drug discovery, biased algorithms may inadvertently favour specific demographic groups, leading to unequal representation and potential exclusion of certain populations.
The war has led to economic sanctions, a surge in commodity prices, and supply chain disruptions, affecting many markets worldwide, including drug discovery. Processes such as high-performance screening and combinatorial chemistry, which are essential to identify potential therapeutic targets, form part of this sector. Also, the loss of access to a large inventory of screening chemicals and on demand, premade library that is essential for the discovery of drugs has been one specific challenge identified during this crisis. Historically, about 80% of the world's screening chemicals used to discover drugs were supplied by companies in Ukraine and Russia, such as Enamine, Life Chemicals or Chem Div. Such a loss of access could be delayed for several months, potentially delaying drug discovery projects.
The impact of the ongoing economic downturn on the market for artificial intelligence in drug discovery is multifaceted, reflecting both challenges and opportunities within the sector. The market for artificial intelligence in drug discovery, driven primarily by the need to reduce research costs and time as well as an increasing incidence of chronic and infectious diseases, has demonstrated resilience and growth despite economic uncertainties. As the urgent need for effective treatments and vaccines against the virus led researchers and pharmaceutical companies to leverage AI technologies at an unprecedented scale, the pandemic, which contributed to the economic slowdown, was a catalyst for the integration of AI into drug discovery. In view of the potential for AI to improve results, reduce costs and accelerate drug discovery processes, this shift in digitalisation and artificial intelligence adoption is expected to be sustained in biomedical and medical research.
In addition, the market for artificial intelligence in drug discovery is characterised by a diverse ecosystem consisting of interdisciplinary research facilities, contract research organizations, CROs and various end users, such as pharmaceutical and biotechnology companies. Technological advances such as machine learning and deep learning, which enable the analysis of complex biological data to be more effective in identifying potential drug candidates, support market expansion.
By Component
Software
Hardware
Services
By Therapeutic Area
Oncology
Neurodegenerative Diseases
Cardiovascular Diseases
Metabolic Diseases
Infectious Diseases
Others
In 2023, the oncology sub-segment held the largest revenue share of over 24.7%. The early detection of disease may be facilitated by the use of AI systems, as diseases are often diagnosed incorrectly due to man error. AI has become more precise in the detection of diseases over recent years. In this fact, lung cancer is usually detected at a later stage, where the survival rate is very low, and early detection with the aid of artificial intelligence systems can prove beneficial. In the scans, a Northwestern University researcher was able to detect lung cancer when no radiologist could find it.
By Application
Drug Optimization and Repurposing
Preclinical Testing
Others
In 2023, the highest revenue share of more than 54.8% was accounted for drug optimisation and repurposing due to the adverse drug reactions and the efficacy of a given medicinal product, advanced artificial intelligence systems, such as Deep Learning and drug modelling, can be used. Advances in artificial intelligence have also facilitated the study and comparison of drugs, so that they can be reutilized to make them more efficient forms with a view to minimising side effects and improving their efficacy. This approach is being adopted by the pharmaceutical industry in order to improve its current products and also incorporate them into new indications, thereby reducing their development costs.
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In 2023, North America accounted for more than 58.6% of total revenue. The United States has been a pioneer in this technology since the inception of artificial intelligence. Utilizing its supercomputer 'Watson,' IBM secured victory in a trivia game called 'Jeopardy,' catalyzing the company's advancements in AI. Since then, AI has evolved into a significant component of the technology industry and is widely implemented across various sectors, including pharmaceuticals. Also, the discovery, design, and reuse of drugs, major technology companies in the U.S. have worked together with prestigious research institutions.
The APAC market is projected to grow at a faster compound annual growth rate than other regions during the forecast period. For the understanding diseases and helping to discover drugs, developing countries in the Asia Pacific region are adopting artificial intelligence. Intuition Systems, an artificial intelligence company in India, was working with Lantern Pharma on drug discovery and biomarker identification. Another of these artificial intelligence companies, Niramai and Sigtuple are dedicated to improving health care through quicker drug discovery and improved identification of targets proteins and biomarkers.
REGIONAL COVERAGE:
North America
US
Canada
Mexico
Europe
Eastern Europe
Poland
Romania
Hungary
Turkey
Rest of Eastern Europe
Western Europe
Germany
France
UK
Italy
Spain
Netherlands
Switzerland
Austria
Rest of Western Europe
Asia Pacific
China
India
Japan
South Korea
Vietnam
Singapore
Australia
Rest of Asia Pacific
Middle East & Africa
Middle East
UAE
Egypt
Saudi Arabia
Qatar
Rest of the Middle East
Africa
Nigeria
South Africa
Rest of Africa
Latin America
Brazil
Argentina
Colombia
Rest of Latin America
Key Players:
The AI in the medication revelation market is divided in nature, with an enormous number of players, including level 1, mid-level organizations, and startup firms, going after portions of the overall industry. The conspicuous players in the worldwide AI in drug revelation market incorporate IBM Corporation, Microsoft, Google, NVIDIA Corporation, Atomwise, Inc., Deep Genomics, Cloud Pharmaceuticals, Insilico Medicine, BenevolentAI, Exscientia, Cyclica, BIOAGE, Numerate, NuMedii, Envisagenics, twoXAR, OWKIN, Inc., XtalPi, Verge Genomics, BERG LLC and Other Players.
Microsoft-Company Financial Analysis
In September 2023, to discover novel small molecule drug candidates in oncology, neuroinflammation and immunology, Exscientia has entered into a partnership with Merck KGaA. The multiyear collaboration will benefit from Exscientia's AI driven precision drug design and discovery capabilities, while leveraging Merck KGaA's disease expertise in the field of oncology or neuroinflammation, as well as clinical skills around the world.
In May 2023, to accelerate drug discovery and precision medicine for biotechnology companies, pharmaceutical companies and public sector organisations, Google Cloud has launched two new AI powered solutions, Target and Lead Identification Suite and Multiomics Suite. More efficient in silico drug design, prediction of protein structures and faster lead optimization for drug discovery are enabled by the Target and Lead Identification Suite.
| Report Attributes | Details |
|---|---|
| Market Size in 2023 | US$ 1.42 Billion |
| Market Size by 2031 | US$ 11.37 Billion |
| CAGR | CAGR of 29.7% From 2024 to 2031 |
| Base Year | 2023 |
| Forecast Period | 2024-2031 |
| Historical Data | 2020-2022 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Product (Manual Tests, Automated Instruments, Consumables & Media) • By Technique (Automated AST, Etest Method, Dilution, Disk Diffusion, Others) • By Application (Drug Development, Susceptibility Testing, Others) • By End-use (Hospitals, Diagnostic Laboratories, Biotechnology & Pharmaceutical Companies, Others) |
| Regional Analysis/Coverage | North America (US, Canada, Mexico), Europe (Eastern Europe [Poland, Romania, Hungary, Turkey, Rest of Eastern Europe] Western Europe] Germany, France, UK, Italy, Spain, Netherlands, Switzerland, Austria, Rest of Western Europe]), Asia Pacific (China, India, Japan, South Korea, Vietnam, Singapore, Australia, Rest of Asia Pacific), Middle East & Africa (Middle East [UAE, Egypt, Saudi Arabia, Qatar, Rest of Middle East], Africa [Nigeria, South Africa, Rest of Africa], Latin America (Brazil, Argentina, Colombia, Rest of Latin America) |
| Company Profiles | IBM Corporation, Microsoft, and Google, NVIDIA Corporation, Atomwise, Inc., Deep Genomics, Cloud Pharmaceuticals, Insilico Medicine, BenevolentAI, Exscientia, Cyclica, BIOAGE, Numerate, NuMedii, Envisagenics, twoXAR, OWKIN, Inc., XtalPi, Verge Genomics, and BERG LLC. |
Ans: The Artificial Intelligence (AI) in Drug Discovery Market was valued at USD 1.42 billion in 2023.
Ans: The expected CAGR of the global Artificial Intelligence (AI) in Drug Discovery Market during the forecast period is 29.7%.
Ans. Neurodegenerative diseases, immuno-oncology, cardiovascular diseases, and metabolic diseases form AI in the drug discovery industry. Due to the growing demand for effective cancer treatment, the immuno-oncology segment holds the highest share of AI in the drug discovery market.
Ans. North America has the largest market share in AI in Drug Discovery Market.
Ans. The drivers of AI in Drug Discovery Market include Increasing Adoption of Artificial Intelligence in Healthcare Sector and growing Investment in Healthcare Sector
TABLE OF CONTENTS
1. Introduction
1.1 Market Definition
1.2 Scope
1.3 Research Assumptions
2. Industry Flowchart
3. Research Methodology
4. Market Dynamics
4.1 Drivers
4.2 Restraints
4.3 Opportunities
4.4 Challenges
5. Impact Analysis
5.1 Impact of Russia-Ukraine Crisis
5.2 Impact of Economic Slowdown on Major Countries
5.2.1 Introduction
5.2.2 United States
5.2.3 Canada
5.2.4 Germany
5.2.5 France
5.2.6 UK
5.2.7 China
5.2.8 Japan
5.2.9 South Korea
5.2.10 India
6. Value Chain Analysis
7. Porter’s 5 Forces Model
8. Pest Analysis
9. Artificial Intelligence (AI) in Drug Discovery Market, By Component
9.1 Introduction
9.2 Trend Analysis
9.3 Software
9.4 Hardware
9.5 Services
10. Artificial Intelligence (AI) in Drug Discovery Market, By Therapeutic Area
10.1 Introduction
10.2 Trend Analysis
10.3 Oncology
10.4 Neurodegenerative Diseases
10.5 Cardiovascular Diseases
10.6 Metabolic Diseases
10.7 Infectious Diseases
10.8 Others
11. Artificial Intelligence (AI) in Drug Discovery Market, By Application
11.1 Introduction
11.2 Trend Analysis
11.3 Drug Optimization and Repurposing
11.4 Preclinical Testing
11.5 Others
12. Regional Analysis
12.1 Introduction
12.2 North America
12.2.1 USA
12.2.2 Canada
12.2.3 Mexico
12.3 Europe
12.3.1 Eastern Europe
12.3.1.1 Poland
12.3.1.2 Romania
12.3.1.3 Hungary
12.3.1.4 Turkey
12.3.1.5 Rest of Eastern Europe
12.3.2 Western Europe
12.3.2.1 Germany
12.3.2.2 France
12.3.2.3 UK
12.3.2.4 Italy
12.3.2.5 Spain
12.3.2.6 Netherlands
12.3.2.7 Switzerland
12.3.2.8 Austria
12.3.2.9 Rest of Western Europe
12.4 Asia-Pacific
12.4.1 China
12.4.2 India
12.4.3 Japan
12.4.4 South Korea
12.4.5 Vietnam
12.4.6 Singapore
12.4.7 Australia
12.4.8 Rest of Asia Pacific
12.5 The Middle East & Africa
12.5.1 Middle East
12.5.1.1 UAE
12.5.1.2 Egypt
12.5.1.3 Saudi Arabia
12.5.1.4 Qatar
12.5.1.5 Rest of the Middle East
11.5.2 Africa
12.5.2.1 Nigeria
12.5.2.2 South Africa
12.5.2.3 Rest of Africa
12.6 Latin America
12.6.1 Brazil
12.6.2 Argentina
12.6.3 Colombia
12.6.4 Rest of Latin America
13. Company Profiles
13.1 IBM Corporation
13.1.1 Company Overview
13.1.2 Financial
13.1.3 Products/ Services Offered
13.1.4 SWOT Analysis
13.1.5 The SNS View
13.2 Microsoft
13.2.1 Company Overview
13.2.2 Financial
13.2.3 Products/ Services Offered
13.2.4 SWOT Analysis
13.2.5 The SNS View
13.3 Google
13.3.1 Company Overview
13.3.2 Financial
13.3.3 Products/ Services Offered
13.3.4 SWOT Analysis
13.3.5 The SNS View
13.4 NVIDIA Corporation
13.4.1 Company Overview
13.4.2 Financial
13.4.3 Products/ Services Offered
13.4.4 SWOT Analysis
13.4.5 The SNS View
13.5 Atomwise, Inc.
13.5.1 Company Overview
13.5.2 Financial
13.5.3 Products/ Services Offered
13.5.4 SWOT Analysis
13.5.5 The SNS View
13.6 Deep Genomics
13.6.1 Company Overview
13.6.2 Financial
13.6.3 Products/ Services Offered
13.6.4 SWOT Analysis
13.6.5 The SNS View
13.7 Cloud Pharmaceuticals
13.7.1 Company Overview
13.7.2 Financial
13.7.3 Products/ Services Offered
13.7.4 SWOT Analysis
13.7.5 The SNS View
13.8 Insilico Medicine
13.8.1 Company Overview
13.8.2 Financial
13.8.3 Products/ Services Offered
13.8.4 SWOT Analysis
13.8.5 The SNS View
13.9 Benevolent AI
13.9.1 Company Overview
13.9.2 Financial
13.9.3 Products/ Services Offered
13.9.4 SWOT Analysis
13.9.5 The SNS View
13.10 Exscientia
13.10.1 Company Overview
13.10.2 Financial
13.10.3 Products/ Services Offered
13.10.4 SWOT Analysis
13.10.5 The SNS View
13.11 Cyclica
13.11.1 Company Overview
13.11.2 Financial
13.11.3 Products/ Services Offered
13.11.4 SWOT Analysis
13.11.5 The SNS View
13.12 OWKIN, Inc.
13.12.1 Company Overview
13.12.2 Financial
13.12.3 Products/ Services Offered
13.12.4 SWOT Analysis
13.12.5 The SNS View
13.13 Verge Genomics
13.13.1 Company Overview
13.13.2 Financial
13.13.3 Products/ Services Offered
13.13.4 SWOT Analysis
13.13.5 The SNS View
13.14 BERG LLC
13.14.1 Company Overview
13.14.2 Financial
13.14.3 Products/ Services Offered
13.14.4 SWOT Analysis
13.14.5 The SNS View
14. Competitive Landscape
14.1 Competitive Benchmarking
14.2 Market Share Analysis
14.3 Recent Developments
14.3.1 Industry News
14.3.2 Company News
14.3.3 Mergers & Acquisitions
15. Use Case and Best Practices
16. Conclusion
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