The Artificial Intelligence in Drug Discovery Market was valued at USD 1.48 billion in 2023 and is expected to reach USD 15.50 billion by 2032 with a growing CAGR of 29.89% over the forecast period of 2024-2032.
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The Artificial Intelligence (AI) in Drug Discovery Market is experiencing remarkable growth, driven by the need for more efficient, faster, and cost-effective drug development processes. Traditional drug discovery is often slow, with an average time of 10-15 years and costs of up to USD 2.6 billion to bring a drug to market. AI technologies, particularly machine learning, deep learning, and natural language processing, can significantly reduce these timelines and costs by streamlining the discovery process and providing valuable insights from complex datasets.
One of the key advantages of AI in drug discovery is its ability to predict molecular interactions and simulate clinical trials. For example, Insilico Medicine, a leader in AI-driven drug discovery, used AI to design a novel drug candidate for fibrosis in just 46 days, a process that typically takes years using conventional methods. Similarly, AI-powered platforms like Atomwise have revolutionized compound screening by analyzing millions of compounds in a fraction of the required time. Atomwise's AI system has been instrumental in identifying potential drug candidates for diseases such as Ebola and Malaria, demonstrating its capacity to discover viable treatments rapidly.
The growing complexity of diseases such as cancer, Alzheimer's, and rare genetic disorders has underscored the need for more advanced discovery tools. AI can handle vast amounts of genetic, clinical, and chemical data to identify new therapeutic targets, enhancing the efficiency of drug development. For instance, Tempus is utilizing AI to personalize cancer treatments by analyzing genomic data to tailor treatment plans based on individual patient profiles, thus improving outcomes and reducing adverse effects.
In 2020, BenevolentAI used AI to identify Baricitinib, an existing rheumatoid arthritis drug, as a potential treatment for COVID-19, leading to its emergency use authorization by regulatory bodies. This capability to rapidly identify existing drugs for new uses highlights the agility AI brings to the drug discovery process.
Additionally, regulatory bodies like the FDA are increasingly providing guidelines for using AI in drug development, facilitating its broader acceptance and integration into the pharmaceutical industry. These advancements in AI technology, along with strategic partnerships between AI startups, pharmaceutical companies, and academic institutions, are helping drive innovation and efficiency, transforming the future of drug discovery.
Drivers
Reducing Costs and Accelerating Drug Discovery
The growing pressure to minimize the time and cost associated with traditional drug discovery is one of the key drivers for AI adoption in the industry. Conventional drug development can take 10-15 years and cost over USD 2.6 billion. AI technologies offer the capability to streamline this process by automating tedious tasks such as data analysis and compound screening. Machine learning and deep learning algorithms can analyze vast datasets, predict molecular interactions, and optimize the selection of drug candidates. This automation not only accelerates drug discovery but also minimizes human error, ensuring more accurate results. AI tools can identify promising drug candidates in a fraction of the time compared to traditional methods, ultimately reducing the cost of bringing a drug to market and increasing the efficiency of the development pipeline.
Addressing Complex Diseases and Enhancing Precision Medicine
The increasing complexity of diseases such as cancer, Alzheimer's, and rare genetic disorders is prompting the adoption of AI to identify novel therapeutic targets and design effective treatments. AI’s ability to process vast amounts of genetic, clinical, and biochemical data enables a deeper understanding of disease mechanisms. By leveraging these insights, AI models can help researchers identify biomarkers and predict how drugs will interact with specific disease pathways. This precision approach is key to developing personalized therapies tailored to individual patients' genetic profiles. The use of AI in this way is crucial for addressing diseases that require more targeted, effective, and personalized treatment strategies, as it accelerates the identification of the most promising therapeutic options.
Leveraging Big Data and Enhancing Drug Discovery Outcomes
The ever-expanding volume of healthcare data, including patient records, clinical trials, and research studies, is another significant factor driving the AI in drug discovery market. With the ability to process and extract meaningful insights from these large datasets, AI technologies are transforming the drug discovery process. Machine learning models can quickly sift through massive amounts of data to identify patterns and correlations that might be missed by traditional methods. This helps researchers make informed decisions about drug candidates, reducing trial and error. Moreover, AI enhances the process of drug repurposing, enabling the identification of existing drugs that could be effective for new indications, further speeding up the discovery process and improving overall outcomes.
Restraints
Data Privacy Concerns and Regulatory Challenges
One of the primary restraints of AI in the Drug Discovery market is the ongoing challenge of data privacy and security, especially when dealing with sensitive patient data. The use of AI in healthcare requires access to large volumes of clinical, genetic, and patient data, raising concerns about the protection of personal health information. Regulatory bodies, including the FDA and EMA, are working on frameworks to ensure AI models meet stringent standards for data protection and privacy. However, these regulations often vary by region, adding complexity to the adoption of AI technologies across global markets. Additionally, the lack of standardized guidelines and ethical considerations regarding the use of AI in drug discovery makes it difficult for companies to navigate the approval processes. These barriers can slow down AI adoption, especially for smaller companies and startups that may face challenges in complying with diverse regulations and ensuring the security of sensitive data in their AI-driven drug development processes.
By Application
In 2023, Drug Optimization and Repurposing was the dominant application segment in the AI in Drug Discovery market which held significant share of 40%. Drug repurposing has become a key focus, especially in response to urgent healthcare needs, as it allows for faster identification of treatments. AI models can quickly analyze existing drug libraries to find new uses for approved medications, dramatically cutting development time and costs. A prime example is BenevolentAI, which used AI to repurpose Baricitinib for COVID-19 treatment. The ability of AI to rapidly identify viable drug candidates for new indications is one of the primary reasons this application remains the dominant force in AI-driven drug discovery.
The Preclinical Testing segment is experiencing the fastest growth within the AI in drug discovery landscape. AI is transforming the preclinical phase by providing innovative tools to predict drug toxicity, side effects, and efficacy earlier in the process. These advancements are minimizing the need for animal testing and enabling more precise preclinical evaluations, speeding up the transition to human clinical trials. This growing demand for faster, cost-effective, and more accurate testing methods is contributing significantly to the rapid growth of this segment.
By Therapeutic Area
In 2023, Oncology accounted for the largest share of the AI-driven drug discovery market, holding 45% of the market. The complexity of cancer treatment, combined with the increasing global burden of cancer cases, has made oncology a primary focus area for AI technologies. AI helps in identifying new therapeutic targets, optimizing clinical trials, and personalizing treatment options based on patients' genetic data. Platforms like Tempus are using AI to integrate genomic data for more effective cancer treatments, which is a driving factor behind oncology's dominance. As the demand for more effective and personalized cancer therapies grows, oncology will continue to be a key area of focus.
The Neurodegenerative Diseases segment is the fastest-growing therapeutic area in AI-driven drug discovery. The increasing global prevalence of diseases like Alzheimer's and Parkinson's has spurred the need for more targeted and effective treatments. AI is enabling more efficient identification of biomarkers and potential drug candidates that can alter the course of these debilitating diseases. By analyzing complex neurological data, AI is helping researchers better understand the underlying mechanisms of these conditions, leading to new therapeutic possibilities. With a greater focus on personalized medicine and early intervention, this segment is rapidly expanding as researchers strive to find solutions for these complex diseases.
In 2023, North America dominated the global AI in Drug Discovery market, accounting for a 57.7% share due to the region's robust healthcare infrastructure, advanced technological landscape, and high levels of investment in pharmaceutical research and development. The United States, in particular, stands out with numerous AI-driven initiatives in drug discovery, led by major companies like IBM Watson Health and Tempus. Government support, including funding for AI research and regulatory efforts to streamline AI adoption in drug discovery, further contributes to North America's dominance. The high concentration of biotech and pharmaceutical companies and academic institutions positions the region at the forefront of AI innovation in drug development.
Asia-Pacific, particularly countries like China and India, is witnessing the fastest AI Drug Discovery market growth. China’s government is heavily investing in AI and biotechnology, aiming to become a global leader in AI-driven healthcare innovation. The region also benefits from a large pool of data and increasing partnerships between technology companies and pharmaceutical firms, driving the rapid adoption of AI technologies in drug discovery.
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IBM Watson for Drug Discovery
Centaur Chemist
Insilico Medicine
PandaOmics
ChemICo
GNS Healthcare (Aitia)
Aitia AI Drug Discovery Platform
Google (DeepMind)
AlphaFold
BenevolentAI Drug Discovery Platform
BioSymetrics, Inc.
BioSymetrics Drug Discovery Platform
Berg Health (BPGbio Inc.)
Interrogative Biology
Atomwise Inc.
AtomNet
Insitro
Insitro Platform
CYCLICA (Acquired by Recursion)
Ligand Express
Cyclica's AI Drug Discovery Platform
Clara Discovery
Schrödinger, Inc.
Schrödinger’s Platform
Microsoft
Project Hanover
Illumina, Inc.
BaseSpace Sequence Hub
Numedii, Inc.
AI Drug Discovery Platform
Xtalpi Inc.
Xtalpi AI Drug Discovery Platform
Iktos
de novo design platform
Tempus
Tempus’ AI-powered platform for Drug Discovery
DEEP GENOMICS
The Deep Genomics AI platform
Verge Genomics
Verge’s AI-driven platform
BenchSci
BenchSci AI-powered platform
Valo Health
Valo Health AI-powered platform
BPGBio, Inc.
BPGBio Drug Discovery Platform
Merck KGaA
Merck’s AI Drug Discovery Platform
IQVIA
IQVIA AI-driven solutions
Tencent Holdings Limited
Tencent AI Drug Discovery Platform
Predictive Oncology, Inc.
TumorGenesis
CytoReason
CytoReason Platform
Owkin, Inc.
Owkin’s AI Platform for Drug Discovery
Cloud Pharmaceuticals
Cloud Pharmaceuticals AI drug design platform
Evaxion Biotech
EVX-01 (AI-driven vaccine development platform)
Standigm
Standigm Drug Discovery Platform
BIOAGE
BIOAGE AI Platform for Age-Related Diseases
Envisagenics
SpliceAI
AbCellera
AbCellera Antibody Discovery Platform
Centella
Centella AI-powered drug discovery platform
In Jan 2025, Advanced Micro Devices (AMD) made a USD 20 million investment in Absci Corp., a company specializing in AI-driven drug discovery. AMD will also collaborate with Absci to support its research initiatives.
In Jan 2025, Collaborations Pharmaceuticals, Inc. (CPI) partnered with Bausch + Lomb to utilize CPI's AI and machine learning capabilities in the discovery of new treatments for eye diseases. The financial details of the collaboration have not been revealed.
In Jan 2025, Adnexus Biotechnologies Inc. launched Trapicolast, a groundbreaking antimalarial drug developed using its AI-powered Sutra platform. This innovative medication targets dual mechanisms in the Plasmodium parasite, focusing on the apicoplast and vesicular trafficking pathways, with the help of AI-discovered ADX1 and ADX2.
| Report Attributes | Details |
| Market Size in 2023 | USD 1.48 Billion |
| Market Size by 2032 | USD 15.50 Billion |
| CAGR | CAGR of 29.89% From 2024 to 2032 |
| Base Year | 2023 |
| Forecast Period | 2024-2032 |
| Historical Data | 2020-2022 |
| Report Scope & Coverage | Market Size, Segments Analysis, Competitive Landscape, Regional Analysis, DROC & SWOT Analysis, Forecast Outlook |
| Key Segments | • By Application [Drug optimization and repurposing, Preclinical testing, Others] • By Therapeutic Area [Oncology, Neurodegenerative Diseases, Cardiovascular Disease, Metabolic Diseases, Infectious Disease, 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, Exscientia, Insilico Medicine, GNS Healthcare (Aitia), Google (DeepMind), BenevolentAI, BioSymetrics, Inc., Berg Health (BPGbio Inc.), Atomwise Inc., Insitro, CYCLICA (Acquired by Recursion), NVIDIA Corporation, Schrödinger, Inc., Microsoft, Illumina, Inc., Numedii, Inc., Xtalpi Inc., Iktos, Tempus, DEEP GENOMICS, Verge Genomics, BenchSci, Valo Health, BPGBio, Inc., Merck KGaA, IQVIA, Tencent Holdings Limited, Predictive Oncology, Inc., CytoReason, Owkin, Inc., Cloud Pharmaceuticals, Evaxion Biotech, Standigm, BIOAGE, Envisagenics, AbCellera, Centella. |
| Key Drivers | • Reducing Costs and Accelerating Drug Discovery • Addressing Complex Diseases and Enhancing Precision Medicine • Leveraging Big Data and Enhancing Drug Discovery Outcomes |
| Restraints | • Data Privacy Concerns and Regulatory Challenges |
Ans: The Artificial Intelligence (AI) in Drug Discovery Market was valued at USD 1.48 billion in 2023.
Ans: The expected CAGR of the global Artificial Intelligence (AI) in Drug Discovery Market during the forecast period is 29.89%.
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 content
1. Introduction
1.1 Market Definition
1.2 Scope (Inclusion and Exclusions)
1.3 Research Assumptions
2. Executive Summary
2.1 Market Overview
2.2 Regional Synopsis
2.3 Competitive Summary
3. Research Methodology
3.1 Top-Down Approach
3.2 Bottom-up Approach
3.3. Data Validation
3.4 Primary Interviews
4. Market Dynamics Impact Analysis
4.1 Market Driving Factors Analysis
4.1.2 Drivers
4.1.2 Restraints
4.1.3 Opportunities
4.1.4 Challenges
4.2 PESTLE Analysis
4.3 Porter’s Five Forces Model
5. Statistical Insights and Trends Reporting
5.1 Technology Adoption, by Region (2020-2032)
5.2 Investment Trends and Funding by Region (2023)
5.3 Regulatory Landscape and Policy Trends (2023)
5.4 Clinical Trial Success Rates (2023)
6. Competitive Landscape
6.1 List of Major Companies, By Region
6.2 Market Share Analysis, By Region
6.3 Service Benchmarking
6.3.1 Service specifications and features
6.3.2 Pricing
6.4 Strategic Initiatives
6.4.1 Marketing and promotional activities
6.4.2 Distribution and Supply Chain Strategies
6.4.3 Expansion plans and new Service launches
6.4.4 Strategic partnerships and collaborations
6.5 Technological Advancements
6.6 Market Positioning and Branding
7. Artificial Intelligence in Drug Discovery Market Segmentation, by Application
7.1 Chapter Overview
7.2 Drug optimization and repurposing
7.2.1 Drug optimization and repurposing Market Trends Analysis (2020-2032)
7.2.2 Drug optimization and repurposing Market Size Estimates and Forecasts to 2032 (USD Billion)
7.3 Preclinical testing
7.3.1 Preclinical Testing Market Trends Analysis (2020-2032)
7.3.2 Preclinical Testing Market Size Estimates and Forecasts to 2032 (USD Billion)
7.4 Others
7.4.1 Others Market Trends Analysis (2020-2032)
7.4.2 Others Market Size Estimates and Forecasts to 2032 (USD Billion)
8. Artificial Intelligence in Drug Discovery Market Segmentation, by Therapeutic Area
8.1 Chapter Overview
8.2 Oncology
8.2.1 Oncology Market Trends Analysis (2020-2032)
8.2.2 Oncology Market Size Estimates and Forecasts to 2032 (USD Billion)
8.3 Neurodegenerative Diseases
8.3.1 Neurodegenerative Diseases Market Trends Analysis (2020-2032)
8.3.2 Neurodegenerative Diseases Market Size Estimates and Forecasts to 2032 (USD Billion)
8.4 Cardiovascular Disease
8.4.1 Cardiovascular Disease Market Trends Analysis (2020-2032)
8.4.2 Cardiovascular Disease Market Size Estimates and Forecasts to 2032 (USD Billion)
8.5 Metabolic Diseases
8.5.1 Metabolic Diseases Market Trends Analysis (2020-2032)
8.5.2 Metabolic Diseases Market Size Estimates and Forecasts to 2032 (USD Billion)
8.6 Infectious Disease
8.6.1 Infectious Disease Market Trends Analysis (2020-2032)
8.6.2 Infectious Disease Market Size Estimates and Forecasts to 2032 (USD Billion)
8.7 Others
8.7.1 Others Market Trends Analysis (2020-2032)
8.7.2 Others Market Size Estimates and Forecasts to 2032 (USD Billion)
9. Regional Analysis
9.1 Chapter Overview
9.2 North America
9.2.1 Trends Analysis
9.2.2 North America Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
9.2.3 North America Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.2.4 North America Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.2.5 USA
9.2.5.1 USA Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.2.5.2 USA Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.2.6 Canada
9.2.6.1 Canada Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.2.6.2 Canada Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.2.7 Mexico
9.2.7.1 Mexico Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.2.7.2 Mexico Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3 Europe
9.3.1 Eastern Europe
9.3.1.1 Trends Analysis
9.3.1.2 Eastern Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
9.3.1.3 Eastern Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.1.4 Eastern Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.1.5 Poland
9.3.1.5.1 Poland Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.1.5.2 Poland Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.1.6 Romania
9.3.1.6.1 Romania Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.1.6.2 Romania Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.1.7 Hungary
9.3.1.7.1 Hungary Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.1.7.2 Hungary Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.1.8 Turkey
9.3.1.8.1 Turkey Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.1.8.2 Turkey Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.1.9 Rest of Eastern Europe
9.3.1.9.1 Rest of Eastern Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.1.9.2 Rest of Eastern Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2 Western Europe
9.3.2.1 Trends Analysis
9.3.2.2 Western Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
9.3.2.3 Western Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.4 Western Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.5 Germany
9.3.2.5.1 Germany Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.5.2 Germany Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.6 France
9.3.2.6.1 France Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.6.2 France Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.7 UK
9.3.2.7.1 UK Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.7.2 UK Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.8 Italy
9.3.2.8.1 Italy Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.8.2 Italy Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.9 Spain
9.3.2.9.1 Spain Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.9.2 Spain Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.10 Netherlands
9.3.2.10.1 Netherlands Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.10.2 Netherlands Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.11 Switzerland
9.3.2.11.1 Switzerland Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.11.2 Switzerland Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.12 Austria
9.3.2.12.1 Austria Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.12.2 Austria Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.3.2.13 Rest of Western Europe
9.3.2.13.1 Rest of Western Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.3.2.13.2 Rest of Western Europe Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4 Asia Pacific
9.4.1 Trends Analysis
9.4.2 Asia Pacific Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
9.4.3 Asia Pacific Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.4.4 Asia Pacific Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4.5 China
9.4.5.1 China Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.4.5.2 China Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4.6 India
9.4.5.1 India Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.4.5.2 India Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4.5 Japan
9.4.5.1 Japan Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.4.5.2 Japan Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4.6 South Korea
9.4.6.1 South Korea Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.4.6.2 South Korea Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4.7 Vietnam
9.4.7.1 Vietnam Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.2.7.2 Vietnam Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4.8 Singapore
9.4.8.1 Singapore Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.4.8.2 Singapore Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4.9 Australia
9.4.9.1 Australia Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.4.9.2 Australia Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.4.10 Rest of Asia Pacific
9.4.10.1 Rest of Asia Pacific Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.4.10.2 Rest of Asia Pacific Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5 Middle East and Africa
9.5.1 Middle East
9.5.1.1 Trends Analysis
9.5.1.2 Middle East Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
9.5.1.3 Middle East Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.1.4 Middle East Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5.1.5 UAE
9.5.1.5.1 UAE Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.1.5.2 UAE Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5.1.6 Egypt
9.5.1.6.1 Egypt Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.1.6.2 Egypt Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5.1.7 Saudi Arabia
9.5.1.7.1 Saudi Arabia Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.1.7.2 Saudi Arabia Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5.1.8 Qatar
9.5.1.8.1 Qatar Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.1.8.2 Qatar Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5.1.9 Rest of Middle East
9.5.1.9.1 Rest of Middle East Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.1.9.2 Rest of Middle East Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5.2 Africa
9.5.2.1 Trends Analysis
9.5.2.2 Africa Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
9.5.2.3 Africa Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.2.4 Africa Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5.2.5 South Africa
9.5.2.5.1 South Africa Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.2.5.2 South Africa Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.5.2.6 Nigeria
9.5.2.6.1 Nigeria Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.5.2.6.2 Nigeria Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.6 Latin America
9.6.1 Trends Analysis
9.6.2 Latin America Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Country (2020-2032) (USD Billion)
9.6.3 Latin America Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.6.4 Latin America Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.6.5 Brazil
9.6.5.1 Brazil Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.6.5.2 Brazil Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.6.6 Argentina
9.6.6.1 Argentina Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.6.6.2 Argentina Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.6.7 Colombia
9.6.7.1 Colombia Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.6.7.2 Colombia Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
9.6.8 Rest of Latin America
9.6.8.1 Rest of Latin America Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Application (2020-2032) (USD Billion)
9.6.8.2 Rest of Latin America Artificial Intelligence in Drug Discovery Market Estimates and Forecasts, by Therapeutic Area (2020-2032) (USD Billion)
10. Company Profiles
10.1 IBM
10.1.1 Company Overview
10.1.2 Financial
10.1.3 Products/ Services Offered
10.1.4 SWOT Analysis
10.2 Exscientia
10.2.1 Company Overview
10.2.2 Financial
10.2.3 Products/ Services Offered
10.2.4 SWOT Analysis
10.3 Insilico Medicine
10.3.1 Company Overview
10.3.2 Financial
10.3.3 Products/ Services Offered
10.3.4 SWOT Analysis
10.4 GNS Healthcare (Aitia)
10.4.1 Company Overview
10.4.2 Financial
10.4.3 Products/ Services Offered
10.4.4 SWOT Analysis
10.5 Google (DeepMind)
10.5.1 Company Overview
10.5.2 Financial
10.5.3 Products/ Services Offered
10.5.4 SWOT Analysis
10.6 BenevolentAI
10.6.1 Company Overview
10.6.2 Financial
10.6.3 Products/ Services Offered
10.6.4 SWOT Analysis
10.7 BioSymetrics, Inc.
10.7.1 Company Overview
10.7.2 Financial
10.7.3 Products/ Services Offered
10.7.4 SWOT Analysis
10.8 NVIDIA Corporation
10.8.1 Company Overview
10.8.2 Financial
10.8.3 Products/ Services Offered
10.8.4 SWOT Analysis
10.9 Microsoft
10.9.1 Company Overview
10.9.2 Financial
10.9.3 Products/ Services Offered
10.9.4 SWOT Analysis
10.10 Illumina, Inc.
10.10.1 Company Overview
10.10.2 Financial
10.10.3 Products/ Services Offered
10.10.4 SWOT Analysis
11. Use Cases and Best Practices
12. Conclusion
An accurate research report requires proper strategizing as well as implementation. There are multiple factors involved in the completion of good and accurate research report and selecting the best methodology to compete the research is the toughest part. Since the research reports we provide play a crucial role in any company’s decision-making process, therefore we at SNS Insider always believe that we should choose the best method which gives us results closer to reality. This allows us to reach at a stage wherein we can provide our clients best and accurate investment to output ratio.
Each report that we prepare takes a timeframe of 350-400 business hours for production. Starting from the selection of titles through a couple of in-depth brain storming session to the final QC process before uploading our titles on our website we dedicate around 350 working hours. The titles are selected based on their current market cap and the foreseen CAGR and growth.
The 5 steps process:
Step 1: Secondary Research:
Secondary Research or Desk Research is as the name suggests is a research process wherein, we collect data through the readily available information. In this process we use various paid and unpaid databases which our team has access to and gather data through the same. This includes examining of listed companies’ annual reports, Journals, SEC filling etc. Apart from this our team has access to various associations across the globe across different industries. Lastly, we have exchange relationships with various university as well as individual libraries.
Step 2: Primary Research
When we talk about primary research, it is a type of study in which the researchers collect relevant data samples directly, rather than relying on previously collected data. This type of research is focused on gaining content specific facts that can be sued to solve specific problems. Since the collected data is fresh and first hand therefore it makes the study more accurate and genuine.
We at SNS Insider have divided Primary Research into 2 parts.
Part 1 wherein we interview the KOLs of major players as well as the upcoming ones across various geographic regions. This allows us to have their view over the market scenario and acts as an important tool to come closer to the accurate market numbers. As many as 45 paid and unpaid primary interviews are taken from both the demand and supply side of the industry to make sure we land at an accurate judgement and analysis of the market.
This step involves the triangulation of data wherein our team analyses the interview transcripts, online survey responses and observation of on filed participants. The below mentioned chart should give a better understanding of the part 1 of the primary interview.
Part 2: In this part of primary research the data collected via secondary research and the part 1 of the primary research is validated with the interviews from individual consultants and subject matter experts.
Consultants are those set of people who have at least 12 years of experience and expertise within the industry whereas Subject Matter Experts are those with at least 15 years of experience behind their back within the same space. The data with the help of two main processes i.e., FGDs (Focused Group Discussions) and IDs (Individual Discussions). This gives us a 3rd party nonbiased primary view of the market scenario making it a more dependable one while collation of the data pointers.
Step 3: Data Bank Validation
Once all the information is collected via primary and secondary sources, we run that information for data validation. At our intelligence centre our research heads track a lot of information related to the market which includes the quarterly reports, the daily stock prices, and other relevant information. Our data bank server gets updated every fortnight and that is how the information which we collected using our primary and secondary information is revalidated in real time.
Step 4: QA/QC Process
After all the data collection and validation our team does a final level of quality check and quality assurance to get rid of any unwanted or undesired mistakes. This might include but not limited to getting rid of the any typos, duplication of numbers or missing of any important information. The people involved in this process include technical content writers, research heads and graphics people. Once this process is completed the title gets uploader on our platform for our clients to read it.
Step 5: Final QC/QA Process:
This is the last process and comes when the client has ordered the study. In this process a final QA/QC is done before the study is emailed to the client. Since we believe in giving our clients a good experience of our research studies, therefore, to make sure that we do not lack at our end in any way humanly possible we do a final round of quality check and then dispatch the study to the client.
By Application
Drug optimization and repurposing
Preclinical testing
Others
By Therapeutic Area
Oncology
Neurodegenerative Diseases
Cardiovascular Disease
Metabolic Diseases
Infectious Disease
Others
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Regional Coverage:
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US
Canada
Mexico
Europe
Eastern Europe
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Romania
Hungary
Turkey
Rest of Eastern Europe
Western Europe
Germany
France
UK
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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
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Available Customization
With the given market data, SNS Insider offers customization to meet the company’s specific needs. The following customization options are available for the report:
Product Analysis
Criss-Cross segment analysis (e.g. Product X Application)
Product Matrix which gives a detailed comparison of the product portfolio of each company
Geographic Analysis
Additional countries in any of the regions
Company Information
Detailed analysis and profiling of additional market players (Up to five)
The Plasma Fractionation Market Size was valued at USD 29.0 Billion in 2023, and is expected to reach USD 48.58 Billion by 2032, and grow at a CAGR of 6.16% over the forecast period 2024-2032.
The Artificial Tears Market size was USD 3.09 Billion in 2023 and is expected to reach USD 5.94 Billion by 2032 and grow at a CAGR of 7.55% over the forecast period of 2024-2032.
The Arrhythmia Monitoring Devices Market Size was valued at USD 7.18 billion in 2023 and is expected to reach USD 13.08 billion by 2032 and grow at a CAGR of 6.9% over the forecast period 2024-2032.
The Medical Consumables Market Size was valued at USD 430.42 Billion in 2023 and is expected to reach USD 1194.52 Billion by 2032 and grow at a CAGR of 12.05% over the forecast period 2024-2032.
The Intraoral Cameras Market size was valued at USD 2.2 billion in 2023 and is expected to reach USD 5.8 billion by 2032 and grow at a CAGR of 11.4% over the forecast period 2024-2032.
The Healthcare CRM Market Size was valued at USD 16.84 billion in 2023 and is expected to reach USD 37.09 billion by 2032 and grow at a CAGR of 9.19%.
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