Artificial Intelligence In Drug Discovery Market
Artificial Intelligence in Drug Discovery Market Forecasts to 2030 - Global Analysis By Component (Software and Services), By Therapeutic Area (Oncology and Small and Other Therapeutic Areas), By Technology (Machine Learning and Other Technologies), Application, End User and By Geography
|
Years Covered |
2021-2030 |
|
Estimated Year Value (2023) |
US $1.4 BN |
|
Projected Year Value (2030) |
US $9.8 BN |
|
CAGR (2023 - 2030) |
31.6% |
|
Regions Covered |
North America, Europe, Asia Pacific, South America, and Middle East & Africa |
|
Countries Covered |
US, Canada, Mexico, Germany, UK, Italy, France, Spain, Japan, China, India, Australia, New Zealand, South Korea, Rest of Asia Pacific, South America, Argentina, Brazil, Chile, Middle East & Africa, Saudi Arabia, UAE, Qatar, and South Africa |
|
Largest Market |
North America |
|
Highest Growing Market |
Asia Pacific |
According to Stratistics MRC, the Global Artificial Intelligence in Drug Discovery Market is accounted for $1.4 billion in 2023 and is expected to reach $9.8 billion by 2030 growing at a CAGR of 31.6% during the forecast period. Artificial intelligence (AI) in the drug discovery market is the application of AI and machine learning techniques to streamline and enhance the drug development process. It utilizes algorithms to analyze vast datasets, predict potential drug candidates, optimize clinical trial designs, and identify novel drug targets. AI accelerates drug discovery by reducing costs, improving the efficiency of research, and increasing the likelihood of identifying successful drug candidates.
According to the International Diabetes Federation (IDF) report, in 2021, approximately 537 million adults (20-79 years) are living with diabetes across the globe. The total number of people living with diabetes is projected to rise to 643 million by 2030 and 783 million by 2045.
Market Dynamics:
Driver:
Rising prevalence of chronic and infectious diseases
AI technologies offer unparalleled capabilities to analyze complex biological data, accelerating drug development processes. With the increasing burden of diseases like cancer, diabetes, and antibiotic-resistant infections, AI aids in the rapid identification of potential drug candidates, target proteins, and treatment strategies. This not only expedites drug discovery but also improves the chances of success in clinical trials, reducing development costs. Furthermore, AI-driven approaches enable the repurposing of existing drugs and facilitate the discovery of novel therapies, ultimately addressing the urgent global healthcare need for more effective treatments.
Restraint:
Lack of data sets in the field of drug discovery
AI heavily relies on vast and diverse data sources for accurate analysis and prediction, but acquiring such data, especially in healthcare, is often challenging due to issues related to privacy, data sharing, and data standardization. Limited access to relevant and well-annotated datasets hinders the training and validation of AI models, potentially leading to suboptimal results and missed opportunities for drug discovery. Addressing these data limitations is crucial for unlocking AI's full potential in accelerating drug development and improving healthcare outcomes.
Opportunity:
Rising prevalence of chronic and infectious diseases
AI-driven solutions are well-suited to address the growing global health crisis by expediting the development of innovative therapeutics. With chronic diseases like cancer and diabetes reaching epidemic proportions and the emergence of antibiotic-resistant infections, AI's data-driven analytics can efficiently identify potential drug candidates, uncover novel targets, and streamline clinical trial designs. By harnessing the power of AI, researchers can accelerate drug discovery processes, optimize personalized treatment strategies, and ultimately, usher in a new era of more effective and accessible therapies to combat the rising burden of these diseases on a global scale.
Threat:
Limited understanding and expertise
The effective application of AI requires interdisciplinary knowledge spanning biology, chemistry, data science, and AI technologies. The shortage of experts who can bridge these domains can hinder the development and deployment of AI-driven solutions for drug discovery. Moreover, misconceptions about the capabilities and limitations of AI may lead to unrealistic expectations. Inadequate understanding can also result in poorly designed experiments or misinterpretation of AI-generated insights, potentially wasting resources and delaying drug development efforts. To harness the full potential of AI, addressing these knowledge gaps and fostering collaboration among experts is essential.
Covid-19 Impact:
The COVID-19 pandemic has had a profound impact on the artificial intelligence in drug discovery market. On one hand, it accelerated the adoption of AI-driven approaches, as researchers urgently sought solutions for drug repurposing and vaccine development. AI played a critical role in identifying potential drug candidates and optimizing clinical trial designs, significantly shortening development timelines. However, the pandemic also disrupted research efforts, delayed clinical trials, and redirected resources, causing setbacks in AI-based drug discovery projects. Moreover, the increased demand for AI expertise and data resources strained the field's capacity, highlighting the need for infrastructure improvements and data sharing initiatives.
The oncology segment is expected to be the largest during the forecast period
The oncology segment is expected to have lucrative growth. AI is revolutionizing oncology drug discovery by rapidly analyzing extensive genomic, proteomic, and clinical data. Machine learning algorithms identify unique genetic mutations, potential drug targets, and predict drug responses, facilitating the development of precision medicines tailored to individual cancer patients. Furthermore, AI enables the repurposing of existing drugs for novel oncology applications, reducing development costs and timelines. With the ever-growing cancer burden worldwide, AI-powered drug discovery offers unprecedented opportunities to uncover groundbreaking therapies, optimize treatment regimens, and improve patient outcomes in the challenging realm of oncology.
The preclinical testing segment is expected to have the highest CAGR during the forecast period
The preclinical testing segment is anticipated to witness the fastest CAGR growth during the forecast period. AI aids in the identification of potential drug candidates by analyzing vast datasets, predicting compound properties, and assessing their safety profiles. Through virtual screening and predictive modelling, AI accelerates the selection of lead compounds for further evaluation, reducing the time and cost associated with preclinical research. Additionally, AI-powered platforms assist in designing more targeted experiments, optimizing study protocols, and predicting potential toxicity issues early in drug development. This innovative approach enhances the efficiency and success rates of preclinical testing, ultimately expediting the delivery of safer and more effective drugs to market.
Region with largest share:
North America holds a significant share in the Artificial Intelligence in Drug Discovery Market, driven by its advanced healthcare infrastructure, strong research and development capabilities, and supportive regulatory environment. The region boasts a high adoption rate of IoT-enabled medical devices, including wearable health trackers and remote monitoring systems, as healthcare providers seek to improve patient care and outcomes. North America's investment in telemedicine and data-driven healthcare, along with its focus on patient-centric care models, positions it as a frontrunner in leveraging IoT technology to transform and enhance the delivery of healthcare services.
Region with highest CAGR:
Asia Pacific is projected to have the highest CAGR over the forecast period, fuelled by its expanding population, increasing healthcare needs, and growing adoption of digital technologies. With the support of government initiatives and a growing tech-savvy consumer base, Artificial Intelligence in Drug Discovery are rapidly gaining acceptance. In addition to improving patient care, they address challenges like remote patient monitoring in rural areas. Asia Pacific's vast market potential, coupled with its commitment to healthcare innovation, positions it as a significant player in the global Artificial Intelligence in Drug Discovery Market, fostering transformative advancements in healthcare delivery.
Key players in the market
Some of the key players in Artificial Intelligence in Drug Discovery market include Cyclica, Deep Genomics, Euretos, Alphabet, Atomwise, Benevolent AI, Berg Health, BioSymetrics, Exscientia, Insilico Medicine, GNS Healthcare, IBM, Insitro, Microsoft, Neumora, Notable, Nvidia Corporation, PathAI and Recursion.
Key Developments:
In November 2022, Exscientia collaborated with the University of Texas MD Anderson Cancer Center to use its patient-centric artificial intelligence technology for novel small molecule drug discovery and development using the expertise of MD Anderson. This strategy helped the company to expand and grow.
In August 2022, GNS Healthcare collaborated with Servier, a global pharmaceutical group to advance drug discovery, translational, and clinical development efforts in multiple myeloma (MM). This strategy helped the company to expand its service offering.
Components Covered:
• Software
• Services
Therapeutic Areas Covered:
• Oncology
• Neurodegenerative Diseases
• Inflammatory
• Infectious Diseases
• Metabolic Diseases
• Rare Diseases
• Cardiovascular Diseases
• Other Therapeutic Areas
Technologies Covered:
• Machine Learning
• Other Technologies
Applications Covered:
• Molecular Library Screening
• Target Identification
• Drug Optimization and Repurposing
• De novo Drug Designing
• Preclinical Testing
End Users Covered:
• Pharmaceutical and Biotechnology Companies
• Contract Research Organization (CROs)
• Academics & Research
• Other End Users
Regions Covered:
• North America
o US
o Canada
o Mexico
• Europe
o Germany
o UK
o Italy
o France
o Spain
o Rest of Europe
• Asia Pacific
o Japan
o China
o India
o Australia
o New Zealand
o South Korea
o Rest of Asia Pacific
• South America
o Argentina
o Brazil
o Chile
o Rest of South America
• Middle East & Africa
o Saudi Arabia
o UAE
o Qatar
o South Africa
o Rest of Middle East & Africa
What our report offers:
- Market share assessments for the regional and country-level segments
- Strategic recommendations for the new entrants
- Covers Market data for the years 2021, 2022, 2023, 2026, and 2030
- Market Trends (Drivers, Constraints, Opportunities, Threats, Challenges, Investment Opportunities, and recommendations)
- Strategic recommendations in key business segments based on the market estimations
- Competitive landscaping mapping the key common trends
- Company profiling with detailed strategies, financials, and recent developments
- Supply chain trends mapping the latest technological advancements
Free Customization Offerings:
All the customers of this report will be entitled to receive one of the following free customization options:
• Company Profiling
o Comprehensive profiling of additional market players (up to 3)
o SWOT Analysis of key players (up to 3)
• Regional Segmentation
o Market estimations, Forecasts and CAGR of any prominent country as per the client's interest (Note: Depends on feasibility check)
• Competitive Benchmarking
Benchmarking of key players based on product portfolio, geographical presence, and strategic alliances
Table of Contents
1 Executive Summary
2 Preface
2.1 Abstract
2.2 Stake Holders
2.3 Research Scope
2.4 Research Methodology
2.4.1 Data Mining
2.4.2 Data Analysis
2.4.3 Data Validation
2.4.4 Research Approach
2.5 Research Sources
2.5.1 Primary Research Sources
2.5.2 Secondary Research Sources
2.5.3 Assumptions
3 Market Trend Analysis
3.1 Introduction
3.2 Drivers
3.3 Restraints
3.4 Opportunities
3.5 Threats
3.6 Technology Analysis
3.7 Application Analysis
3.8 End User Analysis
3.9 Emerging Markets
3.10 Impact of Covid-19
4 Porters Five Force Analysis
4.1 Bargaining power of suppliers
4.2 Bargaining power of buyers
4.3 Threat of substitutes
4.4 Threat of new entrants
4.5 Competitive rivalry
5 Global Artificial Intelligence in Drug Discovery Market, By Component
5.1 Introduction
5.2 Software
5.3 Services
6 Global Artificial Intelligence in Drug Discovery Market, By Therapeutic Area
6.1 Introduction
6.2 Oncology
6.3 Neurodegenerative Diseases
6.4 Inflammatory
6.5 Infectious Diseases
6.6 Metabolic Diseases
6.7 Rare Diseases
6.8 Cardiovascular Diseases
6.9 Other Therapeutic Areas
7 Global Artificial Intelligence in Drug Discovery Market, By Technology
7.1 Introduction
7.2 Machine Learning
7.2.1 Deep Learning
7.2.2 Supervised Learning
7.2.3 Unsupervised Learning
7.2.4 Other Machine Learning Technologies
7.3 Other Technologies
8 Global Artificial Intelligence in Drug Discovery Market, By Application
8.1 Introduction
8.2 Molecular Library Screening
8.3 Target Identification
8.4 Drug Optimization and Repurposing
8.5 De novo Drug Designing
8.6 Preclinical Testing
9 Global Artificial Intelligence in Drug Discovery Market, By End User
9.1 Introduction
9.2 Pharmaceutical and Biotechnology Companies
9.3 Contract Research Organization (CROs)
9.4 Academics & Research
9.5 Other End Users
10 Global Artificial Intelligence in Drug Discovery Market, By Geography
10.1 Introduction
10.2 North America
10.2.1 US
10.2.2 Canada
10.2.3 Mexico
10.3 Europe
10.3.1 Germany
10.3.2 UK
10.3.3 Italy
10.3.4 France
10.3.5 Spain
10.3.6 Rest of Europe
10.4 Asia Pacific
10.4.1 Japan
10.4.2 China
10.4.3 India
10.4.4 Australia
10.4.5 New Zealand
10.4.6 South Korea
10.4.7 Rest of Asia Pacific
10.5 South America
10.5.1 Argentina
10.5.2 Brazil
10.5.3 Chile
10.5.4 Rest of South America
10.6 Middle East & Africa
10.6.1 Saudi Arabia
10.6.2 UAE
10.6.3 Qatar
10.6.4 South Africa
10.6.5 Rest of Middle East & Africa
11 Key Developments
11.1 Agreements, Partnerships, Collaborations and Joint Ventures
11.2 Acquisitions & Mergers
11.3 New Product Launch
11.4 Expansions
11.5 Other Key Strategies
12 Company Profiling
12.1 Cyclica
12.2 Deep Genomics
12.3 Euretos
12.4 Alphabet
12.5 Atomwise
12.6 Benevolent AI
12.7 Berg Health
12.8 BioSymetrics
12.9 Exscientia
12.10 Insilico Medicine
12.11 GNS Healthcare
12.12 IBM
12.13 Insitro
12.14 Microsoft
12.15 Neumora
12.16 Notable
12.17 Nvidia Corporation
12.18 PathAI
12.19 Recursion
List of Tables
1 Global Artificial Intelligence in Drug Discovery Market Outlook, By Region (2021-2030) ($MN)
2 Global Artificial Intelligence in Drug Discovery Market Outlook, By Component (2021-2030) ($MN)
3 Global Artificial Intelligence in Drug Discovery Market Outlook, By Software (2021-2030) ($MN)
4 Global Artificial Intelligence in Drug Discovery Market Outlook, By Services (2021-2030) ($MN)
5 Global Artificial Intelligence in Drug Discovery Market Outlook, By Therapeutic Area (2021-2030) ($MN)
6 Global Artificial Intelligence in Drug Discovery Market Outlook, By Oncology (2021-2030) ($MN)
7 Global Artificial Intelligence in Drug Discovery Market Outlook, By Neurodegenerative Diseases (2021-2030) ($MN)
8 Global Artificial Intelligence in Drug Discovery Market Outlook, By Inflammatory (2021-2030) ($MN)
9 Global Artificial Intelligence in Drug Discovery Market Outlook, By Infectious Diseases (2021-2030) ($MN)
10 Global Artificial Intelligence in Drug Discovery Market Outlook, By Metabolic Diseases (2021-2030) ($MN)
11 Global Artificial Intelligence in Drug Discovery Market Outlook, By Rare Diseases (2021-2030) ($MN)
12 Global Artificial Intelligence in Drug Discovery Market Outlook, By Cardiovascular Diseases (2021-2030) ($MN)
13 Global Artificial Intelligence in Drug Discovery Market Outlook, By Other Therapeutic Areas (2021-2030) ($MN)
14 Global Artificial Intelligence in Drug Discovery Market Outlook, By Technology (2021-2030) ($MN)
15 Global Artificial Intelligence in Drug Discovery Market Outlook, By Machine Learning (2021-2030) ($MN)
16 Global Artificial Intelligence in Drug Discovery Market Outlook, By Deep Learning (2021-2030) ($MN)
17 Global Artificial Intelligence in Drug Discovery Market Outlook, By Supervised Learning (2021-2030) ($MN)
18 Global Artificial Intelligence in Drug Discovery Market Outlook, By Unsupervised Learning (2021-2030) ($MN)
19 Global Artificial Intelligence in Drug Discovery Market Outlook, By Other Machine Learning Technologies (2021-2030) ($MN)
20 Global Artificial Intelligence in Drug Discovery Market Outlook, By Other Technologies (2021-2030) ($MN)
21 Global Artificial Intelligence in Drug Discovery Market Outlook, By Application (2021-2030) ($MN)
22 Global Artificial Intelligence in Drug Discovery Market Outlook, By Molecular Library Screening (2021-2030) ($MN)
23 Global Artificial Intelligence in Drug Discovery Market Outlook, By Target Identification (2021-2030) ($MN)
24 Global Artificial Intelligence in Drug Discovery Market Outlook, By Drug Optimization and Repurposing (2021-2030) ($MN)
25 Global Artificial Intelligence in Drug Discovery Market Outlook, By De novo Drug Designing (2021-2030) ($MN)
26 Global Artificial Intelligence in Drug Discovery Market Outlook, By Preclinical Testing (2021-2030) ($MN)
27 Global Artificial Intelligence in Drug Discovery Market Outlook, By End User (2021-2030) ($MN)
28 Global Artificial Intelligence in Drug Discovery Market Outlook, By Pharmaceutical and Biotechnology Companies (2021-2030) ($MN)
29 Global Artificial Intelligence in Drug Discovery Market Outlook, By Contract Research Organization (CROs) (2021-2030) ($MN)
30 Global Artificial Intelligence in Drug Discovery Market Outlook, By Academics & Research (2021-2030) ($MN)
31 Global Artificial Intelligence in Drug Discovery Market Outlook, By Other End Users (2021-2030) ($MN)
Note: Tables for North America, Europe, APAC, South America, and Middle East & Africa Regions are also represented in the same manner as above.
List of Figures
List of Figures
Figure 1 Global Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 2 North America Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 3 US Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 4 Canada Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 5 Mexico Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 6 Europe Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 7 Germany Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 8 UK Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 9 Italy Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 10 France Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 11 Spain Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 12 Rest of Europe Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 13 Asia Pacific Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 14 Japan Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 15 China Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 16 India Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 17 Australia Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 18 New Zealand Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 19 South Korea Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 20 Rest of Asia Pacific Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 21 South America Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 22 Argentina Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 23 Brazil Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 24 Chile Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 25 Rest of South America Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 26 Middle East & Africa Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 27 Saudi Arabia Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 28 UAE Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 29 Qatar Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 30 South Africa Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
Figure 31 Rest of Middle East & Africa Artificial Intelligence in Drug Discovery Market Outlook (2021-2030) ($MN)
RESEARCH METHODOLOGY
We at ‘Stratistics’ opt for an extensive research approach which involves data mining, data validation, and data analysis. The various research sources include in-house repository, secondary research, competitor’s sources, social media research, client internal data, and primary research.
Our team of analysts prefers the most reliable and authenticated data sources in order to perform the comprehensive literature search. With access to most of the authenticated data bases our team highly considers the best mix of information through various sources to obtain extensive and accurate analysis.
Each report takes an average time of a month and a team of 4 industry analysts. The time may vary depending on the scope and data availability of the desired market report. The various parameters used in the market assessment are standardized in order to enhance the data accuracy.
Data Mining
The data is collected from several authenticated, reliable, paid and unpaid sources and is filtered depending on the scope & objective of the research. Our reports repository acts as an added advantage in this procedure. Data gathering from the raw material suppliers, distributors and the manufacturers is performed on a regular basis, this helps in the comprehensive understanding of the products value chain. Apart from the above mentioned sources the data is also collected from the industry consultants to ensure the objective of the study is in the right direction.
Market trends such as technological advancements, regulatory affairs, market dynamics (Drivers, Restraints, Opportunities and Challenges) are obtained from scientific journals, market related national & international associations and organizations.
Data Analysis
From the data that is collected depending on the scope & objective of the research the data is subjected for the analysis. The critical steps that we follow for the data analysis include:
- Product Lifecycle Analysis
- Competitor analysis
- Risk analysis
- Porters Analysis
- PESTEL Analysis
- SWOT Analysis
The data engineering is performed by the core industry experts considering both the Marketing Mix Modeling and the Demand Forecasting. The marketing mix modeling makes use of multiple-regression techniques to predict the optimal mix of marketing variables. Regression factor is based on a number of variables and how they relate to an outcome such as sales or profits.
Data Validation
The data validation is performed by the exhaustive primary research from the expert interviews. This includes telephonic interviews, focus groups, face to face interviews, and questionnaires to validate our research from all aspects. The industry experts we approach come from the leading firms, involved in the supply chain ranging from the suppliers, distributors to the manufacturers and consumers so as to ensure an unbiased analysis.
We are in touch with more than 15,000 industry experts with the right mix of consultants, CEO's, presidents, vice presidents, managers, experts from both supply side and demand side, executives and so on.
The data validation involves the primary research from the industry experts belonging to:
- Leading Companies
- Suppliers & Distributors
- Manufacturers
- Consumers
- Industry/Strategic Consultants
Apart from the data validation the primary research also helps in performing the fill gap research, i.e. providing solutions for the unmet needs of the research which helps in enhancing the reports quality.
For more details about research methodology, kindly write to us at info@strategymrc.com
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