AI for Drug Discovery Market Size, Share, and Trends Analysis Report
CAGR :
| Market Size 2023 (Base Year) | USD 924.15 Million |
| Market Size 2032 (Forecast Year) | USD 17487.97 Million |
| CAGR | 38.64% |
| Forecast Period | 2024 - 2032 |
| Historical Period | 2018 - 2023 |
AI for Drug Discovery Market Insights
According to Market Research Store, the global AI for drug discovery market size was valued at around USD 924.15 million in 2023 and is estimated to reach USD 17487.97 million by 2032, to register a CAGR of approximately 38.64% in terms of revenue during the forecast period 2024-2032.
The AI for drug discovery report provides a comprehensive analysis of the market, including its size, share, growth trends, revenue details, and other crucial information regarding the target market. It also covers the drivers, restraints, opportunities, and challenges till 2032.
Global AI for Drug Discovery Market: Overview
AI for Drug Discovery refers to the application of artificial intelligence technologies, such as machine learning, deep learning, and natural language processing, to streamline and enhance the drug development process. This involves using AI algorithms to predict molecular interactions, identify potential drug candidates, optimize chemical structures, and analyze vast datasets from clinical trials and biomedical research. By automating complex tasks, AI reduces the time, cost, and failure rates traditionally associated with drug discovery, allowing for faster identification of promising compounds and personalized therapies.
Key Highlights
- The AI for drug discovery market is anticipated to grow at a CAGR of 38.64% during the forecast period.
- The global AI for drug discovery market was estimated to be worth approximately USD 924.15 million in 2023 and is projected to reach a value of USD 17487.97 million by 2032.
- The growth of the AI for drug discovery market is being driven by increasing demand for precision medicine, the rising complexity of drug development, and advancements in computational power and AI technologies.
- Based on the drug type, the small molecule segment is growing at a high rate and is projected to dominate the market.
- On the basis of technology, the machine learning segment is projected to swipe the largest market share.
- In terms of therapeutic area, the oncology segment is expected to dominate the market.
- Based on the end user, the pharmaceutical companies segment is expected to dominate the market.
- By region, North America is expected to dominate the global market during the forecast period.
AI for Drug Discovery Market: Dynamics
Key Growth Drivers
The AI for Drug Discovery market is experiencing significant growth, propelled by several powerful factors. The primary catalyst is the urgent need to address the immense cost and protracted timelines associated with traditional pharmaceutical research and development. The industry faces a productivity paradox, where R&D spending has soared while the number of new drug approvals has not kept pace. AI offers a solution by dramatically accelerating key stages, such as target identification and lead compound optimization, potentially saving years and billions of dollars. This is further enabled by the explosion of complex biological, chemical, and clinical data, which provides the essential fuel for AI algorithms to uncover hidden patterns and generate novel insights that are beyond human-scale analysis. Consequently, there has been a substantial influx of investment from venture capital and established pharmaceutical giants, who are actively forming strategic partnerships with AI-biotech firms to leverage this transformative technology.
Key Restraints
Despite its potential, the widespread adoption of AI in drug discovery is tempered by notable restraints. A fundamental challenge lies in the issue of data quality and accessibility, as AI models are highly dependent on vast amounts of standardized, well-annotated data. The life sciences sector often struggles with siloed, heterogeneous, and poorly structured datasets, which can compromise the accuracy and generalizability of AI predictions. Furthermore, a landscape of regulatory uncertainty presents a significant hurdle, as agencies like the FDA are still evolving their frameworks for evaluating drugs discovered or developed using AI, creating a validation gap for companies seeking approval. This is compounded by a critical shortage of interdisciplinary talent possessing deep expertise in both machine learning and molecular biology, creating a bottleneck for effective research and development.
Key Opportunities
The market is poised to capitalize on several high-impact opportunities that extend beyond incremental efficiency gains. The most promising frontier is the rise of generative AI, which can move beyond analyzing existing data to proactively design novel drug-like molecules and antibodies with optimized properties, thereby venturing into uncharted chemical space. Another significant opportunity lies in the application of AI to advance personalized medicine, where it can analyze multi-omics data and electronic health records to identify biomarkers and match specific patient subpopulations with the therapies most likely to benefit them. AI also offers a powerful strategy for drug repurposing by systematically analyzing vast datasets to identify new therapeutic applications for existing approved drugs, offering a faster and more cost-effective path to clinical use.
Key Challenges
Successfully integrating AI into the core of drug discovery involves navigating a set of complex and persistent challenges. A central scientific and regulatory hurdle is the "black box" problem, where the most powerful AI models often lack interpretability, making it difficult for scientists and regulators to understand the rationale behind a proposed compound, which is crucial for building trust and ensuring safety. Another major challenge is the seamless integration of these new digital tools into the well-established, multi-stage workflows of pharmaceutical companies, which often face cultural and operational resistance to changing long-standing practices. There is also an inherent risk of algorithmic bias, where models trained on non-representative or incomplete data may yield drug candidates that are less effective or even unsafe for underrepresented demographic groups, perpetuating health disparities.
AI for Drug Discovery Market: Report Scope
| Report Attributes | Report Details |
|---|---|
| Report Name | AI for Drug Discovery Market |
| Market Size in 2023 | USD 924.15 Million |
| Market Forecast in 2032 | USD 17487.97 Million |
| Growth Rate | CAGR of 38.64% |
| Number of Pages | 195 |
| Key Companies Covered | BionureFarma, S.L., Novartis Pharmaceuticals Corporation, Genervon Biopharmaceuticals, LLC,Insilico Medicine, Numerate, Recursion Pharmaceuticals, Biogen,Silicon Therapeutics,BenevolentAI,Astrocyte Pharmaceuticals, Inc.,NeuroVive Pharmaceutical AB, Teva Pharmaceutical Industries Ltd., AstraZeneca,Envisagenics, Inc., twoXAR Incorporated, BIOAGE,Neuren Pharmaceuticals,Atomwise, Cloud Pharmaceuticals, Inc.,Hoffmann-La Roche Ltd., Exscientia, and Accutar Biotech |
| Segments Covered | By Deployment Type, By End-User, And By Region |
| Regions Covered | North America, Europe, Asia Pacific (APAC), Latin America, Middle East, and Africa (MEA) |
| Base Year | 2023 |
| Historical Year | 2018 to 2023 |
| Forecast Year | 2024 to 2032 |
| Customization Scope | Avail customized purchase options to meet your exact research needs. Request For Customization |
AI for Drug Discovery Market: Segmentation Insights
The global AI for drug discovery market is divided by drug type, technology, therapeutic area, end user, and region.
Based on drug type, the global AI for drug discovery market is divided into small molecule and large molecule. The small molecule segment currently dominates the AI-driven drug discovery market. This dominance is attributed to the extensive availability of clinical data, predictable pharmacokinetic properties, and cost-effectiveness. Small molecules have been the cornerstone of pharmaceutical development, comprising over 90% of the global pharmaceutical market, with a significant number of new chemical entities approved by regulatory agencies. The large molecule (biologics) segment is experiencing rapid growth. Advancements in biotechnology and increased investment in biologic therapies are driving this expansion. AI plays a crucial role in navigating the complex structural and functional attributes of biologics, optimizing their development. Biologics, including monoclonal antibodies and gene therapies, are particularly well-suited to AI-augmented development due to the many physicochemical properties that must be optimized, creating a highly complex and multidimensional search space.
On the basis of technology, the global AI for drug discovery market is bifurcated into deep learning, machine learning, and others. The Machine Learning (ML) segment dominates the AI-driven drug discovery market. ML algorithms are highly effective in analyzing vast datasets, including biological, chemical, and clinical data, to identify potential drug candidates, predict their efficacy and safety profiles, and optimize clinical trial designs. This capability significantly improves decision-making, reduces drug discovery failure rates, and enhances overall efficiency. The Deep Learning (DL) segment is growing rapidly due to its ability to model complex patterns in large datasets. Techniques like convolutional neural networks (CNNs) and generative adversarial networks (GANs) are particularly useful for tasks such as protein structure prediction, molecular generation, and drug-target interaction modeling, accelerating various stages of drug development.
By therapeutic area, the global AI for drug discovery market is divided into metabolic diseases, immuno-oncology, neurodegenerative diseases, cardiovascular diseases, oncology, and others. The Oncology segment dominates the AI-driven drug discovery market by therapeutic area. AI is extensively applied in oncology to identify novel drug candidates, predict tumor responses, and optimize treatment strategies. The complexity of cancer biology, coupled with large datasets from genomics, proteomics, and clinical trials, makes AI particularly valuable in accelerating drug discovery and improving therapeutic outcomes in this area. The Immuno-Oncology segment is rapidly growing as AI helps design therapies that enhance the immune system’s ability to target cancer cells. AI models assist in predicting patient-specific responses and optimizing combination therapies, supporting the development of personalized treatments.
In tems of end user, the global AI for drug discovery market is bifurcated into pharmaceutical companies, academic & research institutes, biopharmaceutical companies, and others. The Pharmaceutical Companies segment dominates the AI-driven drug discovery market by end user. These companies leverage AI to accelerate the identification and optimization of drug candidates, streamline clinical trials, and reduce overall R&D costs. The integration of AI enables them to enhance efficiency, improve decision-making, and maintain a competitive edge in drug development. The Biopharmaceutical Companies segment is growing rapidly as these firms adopt AI to develop biologics, gene therapies, and personalized medicines. AI helps in analyzing complex biological data, predicting therapeutic efficacy, and optimizing production processes, supporting innovation in biologic drug development.
AI for Drug Discovery Market: Regional Insights
- North America is expected to dominates the global market
North America stands as the dominant force in the AI-driven drug discovery market, primarily due to its robust infrastructure, substantial investments in research and development, and the presence of leading pharmaceutical and biotechnology companies. The United States, in particular, has been at the forefront, with numerous collaborations between tech firms and healthcare institutions, fostering an environment ripe for innovation. This region's early adoption of AI technologies and its well-established regulatory frameworks have significantly accelerated the integration of AI in drug discovery processes.
Europe follows closely, with countries like Germany, the United Kingdom, and Switzerland making significant strides in AI applications within the pharmaceutical sector. The European Union's emphasis on digital transformation and personalized medicine has led to increased funding and initiatives aimed at integrating AI into drug discovery. Collaborations between academic institutions and industry players have further bolstered the region's position, making it a key player in the global market.
Asia Pacific is experiencing rapid growth in AI-driven drug discovery, propelled by nations such as China, India, and Japan. Government initiatives promoting digital health and AI research, coupled with a burgeoning biotech industry, have created a conducive environment for the adoption of AI technologies. China's significant investments in AI research and India's expanding pharmaceutical sector are particularly noteworthy, positioning the region as a formidable competitor in the global landscape.
Latin America, while emerging, is gradually integrating AI into its pharmaceutical research and development processes. Countries like Brazil and Mexico are beginning to recognize the potential of AI in enhancing drug discovery, leading to increased collaborations and investments. However, the region still faces challenges such as limited infrastructure and regulatory hurdles, which may impede the rapid adoption of AI technologies.
Middle East and Africa (MEA) is in the nascent stages of adopting AI in drug discovery. While there is a growing interest, particularly in countries like the United Arab Emirates and South Africa, the lack of established infrastructure and limited funding pose significant challenges. Nonetheless, ongoing efforts to improve healthcare systems and invest in digital technologies may pave the way for future growth in this sector.
Recent Developments:
- In January 2023, BioNTech SE completed the acquisition of InstaDeep Ltd., a global AI and ML technology leader, to strengthen its AI-driven drug discovery and develop next-generation immunotherapies and vaccines for high-need diseases.
AI for Drug Discovery Market: Competitive Landscape
The report provides an in-depth analysis of companies operating in the AI for drug discovery market, including their geographic presence, business strategies, product offerings, market share, and recent developments. This analysis helps to understand market competition.
Some of the major players in the global AI for drug discovery market include:
- BionureFarma S.L.
- Novartis Pharmaceuticals Corporation
- Genervon Biopharmaceuticals LLC
- Insilico Medicine
- Numerate
- Recursion Pharmaceuticals
- Biogen
- Silicon Therapeutics
- BenevolentAI
- Astrocyte Pharmaceuticals Inc.
- NeuroVive Pharmaceutical AB
- Teva Pharmaceutical Industries Ltd.
- AstraZeneca
- Envisagenics Inc.
- twoXAR Incorporated
- BIOAGE
- Neuren Pharmaceuticals
- Atomwise
- Cloud Pharmaceuticals Inc.
- Hoffmann-La Roche Ltd.
- Exscientia
- Accutar Biotech
The global AI for drug discovery market is segmented as follows:
By Drug Type
- Small Molecule
- Large Molecule
By Technology
- Deep Learning
- Machine Learning
- Others
By Therapeutic Area
- Metabolic Diseases
- Immuno-Oncology
- Neurodegenerative Diseases
- Cardiovascular Diseases
- Oncology
- Others
By End User
- Pharmaceutical Companies
- Academic & Research Institutes
- Biopharmaceutical Companies
- Others
By Region
- North America
- U.S.
- Canada
- Europe
- U.K.
- France
- Germany
- Rest of Europe
- Asia Pacific
- China
- Japan
- India
- South Korea
- Rest of Asia Pacific
- Latin America
- Brazil
- Rest of Latin America
- The Middle East and Africa
- GCC Countries
- South Africa
- Rest of Middle East Africa
Frequently Asked Questions
Table Of Content
CHAPTER 1. Executive Summary 23 CHAPTER 2. AI for Drug Discovery market – Drug Type Analysis 26 2.1. Global AI for Drug Discovery Market – Drug Type Overview 26 2.2. Global AI for Drug Discovery Market Share, by Drug Type, 2018 & 2025 (USD Million) 26 2.3. Small Molecule 28 2.3.1. Global Small Molecule AI for Drug Discovery Market, 2015-2027 (USD Million) 28 2.4. Large Molecule 29 2.4.1. Global Large Molecule AI for Drug Discovery Market, 2015-2027 (USD Million) 29 CHAPTER 3. AI for Drug Discovery market – Technology Analysis 29 3.1. Global AI for Drug Discovery Market – Technology Overview 29 3.2. Global AI for Drug Discovery Market Share, by Technology, 2018 & 2025 (USD Million) 30 3.3. Deep Learning 31 3.3.1. Global Deep Learning AI for Drug Discovery Market, 2015-2027 (USD Million) 31 3.4. Machine Learning 32 3.4.1. Global Machine Learning AI for Drug Discovery Market, 2015-2027 (USD Million) 32 3.5. Others 33 3.5.1. Global Others AI for Drug Discovery Market, 2015-2027 (USD Million) 33 CHAPTER 4. AI for Drug Discovery market – Therapeutic Area Analysis 33 4.1. Global AI for Drug Discovery Market – Therapeutic Area Overview 33 4.2. Global AI for Drug Discovery Market Share, by Therapeutic Area, 2018 & 2025 (USD Million) 34 4.3. Metabolic Diseases 35 4.3.1. Global Metabolic Diseases AI for Drug Discovery Market, 2015-2027 (USD Million) 35 4.4. Immuno-Oncology 36 4.4.1. Global Immuno-Oncology AI for Drug Discovery Market, 2015-2027 (USD Million) 36 4.5. Neurodegenerative Diseases 37 4.5.1. Global Neurodegenerative Diseases AI for Drug Discovery Market, 2015-2027 (USD Million) 37 4.6. Cardiovascular Diseases 38 4.6.1. Global Cardiovascular Diseases AI for Drug Discovery Market, 2015-2027 (USD Million) 38 4.7. Oncology 39 4.7.1. Global Oncology AI for Drug Discovery Market, 2015-2027 (USD Million) 39 4.8. Others 40 4.8.1. Global Others AI for Drug Discovery Market, 2015-2027 (USD Million) 40 CHAPTER 5. AI for Drug Discovery market – End User Analysis 40 5.1. Global AI for Drug Discovery Market – End User Overview 40 5.2. Global AI for Drug Discovery Market Share, by End User, 2018 & 2025 (USD Million) 41 5.3. Pharmaceutical Companies 42 5.3.1. Global Pharmaceutical Companies AI for Drug Discovery Market, 2015-2027 (USD Million) 42 5.4. Academic & Research Institutes 43 5.4.1. Global Academic & Research Institutes AI for Drug Discovery Market, 2015-2027 (USD Million) 43 5.5. Biopharmaceutical Companies 44 5.5.1. Global Biopharmaceutical Companies AI for Drug Discovery Market, 2015-2027 (USD Million) 44 5.6. Others 45 5.6.1. Global Others AI for Drug Discovery Market, 2015-2027 (USD Million) 45 CHAPTER 6. AI for Drug Discovery market – Regional Analysis 46 6.1. Global AI for Drug Discovery Market Regional Overview 46 6.2. Global AI for Drug Discovery Market Share, by Region, 2018 & 2025 (Value) 46 6.3. North America 48 6.3.1. North America AI for Drug Discovery Market size and forecast, 2015-2027 48 6.3.2. North America AI for Drug Discovery Market, by Country, 2018 & 2025 (USD Million) 48 6.3.3. North America AI for Drug Discovery Market, by Drug Type, 2015-2027 50 6.3.3.1. North America AI for Drug Discovery Market, by Drug Type, 2015-2027 (USD Million) 50 6.3.4. North America AI for Drug Discovery Market, by Technology, 2015-2027 51 6.3.4.1. North America AI for Drug Discovery Market, by Technology, 2015-2027 (USD Million) 51 6.3.5. North America AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 52 6.3.5.1. North America AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 (USD Million) 52 6.3.6. North America AI for Drug Discovery Market, by End User, 2015-2027 53 6.3.6.1. North America AI for Drug Discovery Market, by End User, 2015-2027 (USD Million) 53 6.3.7. U.S. 54 6.3.7.1. U.S. Market size and forecast, 2015-2027 (USD Million) 54 6.3.8. Canada 55 6.3.8.1. Canada Market size and forecast, 2015-2027 (USD Million) 55 6.3.9. Mexico 56 6.3.9.1. Mexico Market size and forecast, 2015-2027 (USD Million) 56 6.4. Europe 57 6.4.1. Europe AI for Drug Discovery Market size and forecast, 2015-2027 57 6.4.2. Europe AI for Drug Discovery Market, by Country, 2018 & 2025 (USD Million) 57 6.4.3. Europe AI for Drug Discovery Market, by Drug Type, 2015-2027 59 6.4.3.1. Europe AI for Drug Discovery Market, by Drug Type, 2015-2027 (USD Million) 59 6.4.4. Europe AI for Drug Discovery Market, by Technology, 2015-2027 60 6.4.4.1. Europe AI for Drug Discovery Market, by Technology, 2015-2027 (USD Million) 60 6.4.5. Europe AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 61 6.4.5.1. Europe AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 (USD Million) 61 6.4.6. Europe AI for Drug Discovery Market, by End User, 2015-2027 62 6.4.6.1. Europe AI for Drug Discovery Market, by End User, 2015-2027 (USD Million) 62 6.4.7. Germany 63 6.4.7.1. Germany Market size and forecast, 2015-2027 (USD Million) 63 6.4.8. France 64 6.4.8.1. France Market size and forecast, 2015-2027 (USD Million) 64 6.4.9. U.K. 65 6.4.9.1. U.K. Market size and forecast, 2015-2027 (USD Million) 65 6.4.10. Italy 66 6.4.10.1. Italy Market size and forecast, 2015-2027 (USD Million) 66 6.4.11. Spain 67 6.4.11.1. Spain Market size and forecast, 2015-2027 (USD Million) 67 6.4.12. Nordic Countries 68 6.4.12.1. Nordic Countries Market size and forecast, 2015-2027 (USD Million) 68 6.4.13. Benelux Union 69 6.4.13.1. Benelux Union Market size and forecast, 2015-2027 (USD Million) 69 6.4.14. Rest of Europe 70 6.4.14.1. Rest of Europe Market size and forecast, 2015-2027 (USD Million) 70 6.5. Asia Pacific 71 6.5.1. Asia Pacific AI for Drug Discovery Market size and forecast, 2015-2027 71 6.5.2. Asia Pacific AI for Drug Discovery Market, by Country, 2018 & 2025 (USD Million) 71 6.5.3. Asia Pacific AI for Drug Discovery Market, by Drug Type, 2015-2027 73 6.5.3.1. Asia Pacific AI for Drug Discovery Market, by Drug Type, 2015-2027 (USD Million) 73 6.5.4. Asia Pacific AI for Drug Discovery Market, by Technology, 2015-2027 74 6.5.4.1. Asia Pacific AI for Drug Discovery Market, by Technology, 2015-2027 (USD Million) 74 6.5.5. Asia Pacific AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 75 6.5.5.1. Asia Pacific AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 (USD Million) 75 6.5.6. Asia Pacific AI for Drug Discovery Market, by End User, 2015-2027 76 6.5.6.1. Asia Pacific AI for Drug Discovery Market, by End User, 2015-2027 (USD Million) 76 6.5.7. China 77 6.5.7.1. China Market size and forecast, 2015-2027 (USD Million) 77 6.5.8. Japan 78 6.5.8.1. Japan Market size and forecast, 2015-2027 (USD Million) 78 6.5.9. India 79 6.5.9.1. India Market size and forecast, 2015-2027 (USD Million) 79 6.5.10. New Zealand 80 6.5.10.1. New Zealand Market size and forecast, 2015-2027 (USD Million) 80 6.5.11. Australia 81 6.5.11.1. Australia Market size and forecast, 2015-2027 (USD Million) 81 6.5.12. South Korea 82 6.5.12.1. South Korea Market size and forecast, 2015-2027 (USD Million) 82 6.5.13. South-East Asia 83 6.5.13.1. South-East Asia Market size and forecast, 2015-2027 (USD Million) 83 6.5.14. Rest of Asia Pacific 84 6.5.14.1. Rest of Asia Pacific Market size and forecast, 2015-2027 (USD Million) 84 6.6. Latin America 85 6.6.1. Latin America AI for Drug Discovery Market size and forecast, 2015-2027 85 6.6.2. Latin America AI for Drug Discovery Market, by Country, 2018 & 2025 (USD Million) 85 6.6.3. Latin America AI for Drug Discovery Market, by Drug Type, 2015-2027 87 6.6.3.1. Latin America AI for Drug Discovery Market, by Drug Type, 2015-2027 (USD Million) 87 6.6.4. Latin America AI for Drug Discovery Market, by Technology, 2015-2027 88 6.6.4.1. Latin America AI for Drug Discovery Market, by Technology, 2015-2027 (USD Million) 88 6.6.5. Latin America AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 89 6.6.5.1. Latin America AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 (USD Million) 89 6.6.6. Latin America AI for Drug Discovery Market, by End User, 2015-2027 90 6.6.6.1. Latin America AI for Drug Discovery Market, by End User, 2015-2027 (USD Million) 90 6.6.7. Brazil 91 6.6.7.1. Brazil Market size and forecast, 2015-2027 (USD Million) 91 6.6.8. Argentina 92 6.6.8.1. Argentina Market size and forecast, 2015-2027 (USD Million) 92 6.6.9. Rest of Latin America 93 6.6.9.1. Rest of Latin America Market size and forecast, 2015-2027 (USD Million) 93 6.7. The Middle-East and Africa 94 6.7.1. The Middle-East and Africa AI for Drug Discovery Market size and forecast, 2015-2027 94 6.7.2. The Middle-East and Africa AI for Drug Discovery Market, by Country, 2018 & 2025 (USD Million) 94 6.7.3. The Middle-East and Africa AI for Drug Discovery Market, by Drug Type, 2015-2027 96 6.7.3.1. The Middle-East and Africa AI for Drug Discovery Market, by Drug Type, 2015-2027 (USD Million) 96 6.7.4. The Middle-East and Africa AI for Drug Discovery Market, by Technology, 2015-2027 97 6.7.4.1. The Middle-East and Africa AI for Drug Discovery Market, by Technology, 2015-2027 (USD Million) 97 6.7.5. The Middle-East and Africa AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 98 6.7.5.1. The Middle-East and Africa AI for Drug Discovery Market, by Therapeutic Area, 2015-2027 (USD Million) 98 6.7.6. The Middle-East and Africa AI for Drug Discovery Market, by End User, 2015-2027 99 6.7.6.1. The Middle-East and Africa AI for Drug Discovery Market, by End User, 2015-2027 (USD Million) 99 6.7.7. Saudi Arabia 100 6.7.7.1. Saudi Arabia Market size and forecast, 2015-2027 (USD Million) 100 6.7.8. UAE 101 6.7.8.1. UAE Market size and forecast, 2015-2027 (USD Million) 101 6.7.9. Egypt 102 6.7.9.1. Egypt Market size and forecast, 2015-2027 (USD Million) 102 6.7.10. Kuwait 103 6.7.10.1. Kuwait Market size and forecast, 2015-2027 (USD Million) 103 6.7.11. South Africa 104 6.7.11.1. South Africa Market size and forecast, 2015-2027 (USD Million) 104 6.7.12. Rest of Middle-East Africa 105 6.7.12.1. Rest of Middle-East Africa Market size and forecast, 2015-2027 (USD Million) 105 CHAPTER 7. AI for Drug Discovery market – Competitive Landscape 106 7.1. Competitor Market Share – Revenue 106 7.2. Market Concentration Rate Analysis, Top 3 and Top 5 Players 108 7.3. Strategic Development 109 7.3.1. Acquisitions and Mergers 109 7.3.2. New Products 109 7.3.3. Research & Development Activities 109 CHAPTER 8. Company Profiles 110 8.1. Recursion Pharmaceuticals 110 8.1.1. Company Overview 110 8.1.2. Recursion Pharmaceuticals Revenue and Gross Margin 110 8.1.3. Product portfolio 111 8.1.4. Recent initiatives 112 8.2. AstraZeneca 112 8.2.1. Company Overview 112 8.2.2. AstraZeneca Revenue and Gross Margin 112 8.2.3. Product portfolio 113 8.2.4. Recent initiatives 114 8.3. Astrocyte Pharmaceuticals Inc. 114 8.3.1. Company Overview 114 8.3.2. Astrocyte Pharmaceuticals Inc. Revenue and Gross Margin 114 8.3.3. Product portfolio 115 8.3.4. Recent initiatives 116 8.4. NeuroVive Pharmaceutical AB 116 8.4.1. Company Overview 116 8.4.2. NeuroVive Pharmaceutical AB Revenue and Gross Margin 116 8.4.3. Product portfolio 117 8.4.4. Recent initiatives 118 8.5. twoXAR Incorporated 118 8.5.1. Company Overview 118 8.5.2. twoXAR Incorporated Revenue and Gross Margin 118 8.5.3. Product portfolio 119 8.5.4. Recent initiatives 120 8.6. Biogen 120 8.6.1. Company Overview 120 8.6.2. Biogen Revenue and Gross Margin 120 8.6.3. Product portfolio 121 8.6.4. Recent initiatives 122 8.7. Bionure Farma S.L. 122 8.7.1. Company Overview 122 8.7.2. Bionure Farma S.L. Revenue and Gross Margin 122 8.7.3. Product portfolio 123 8.7.4. Recent initiatives 124 8.8. BIOAGE 124 8.8.1. Company Overview 124 8.8.2. BIOAGE Revenue and Gross Margin 124 8.8.3. Product portfolio 125 8.8.4. Recent initiatives 126 8.9. Genervon Biopharmaceuticals LLC 126 8.9.1. Company Overview 126 8.9.2. Genervon Biopharmaceuticals LLC Revenue and Gross Margin 126 8.9.3. Product portfolio 127 8.9.4. Recent initiatives 128 8.10. Hoffmann-La Roche Ltd. 128 8.10.1. Company Overview 128 8.10.2. Hoffmann-La Roche Ltd. Revenue and Gross Margin 128 8.10.3. Product portfolio 129 8.10.4. Recent initiatives 130 8.11. Neuren Pharmaceuticals 130 8.11.1. Company Overview 130 8.11.2. Neuren Pharmaceuticals Revenue and Gross Margin 130 8.11.3. Product portfolio 131 8.11.4. Recent initiatives 132 8.12. Atomwise 132 8.12.1. Company Overview 132 8.12.2. Atomwise Revenue and Gross Margin 132 8.12.3. Product portfolio 133 8.12.4. Recent initiatives 134 8.13. Insilico Medicine 134 8.13.1. Company Overview 134 8.13.2. Insilico Medicine Revenue and Gross Margin 134 8.13.3. Product portfolio 135 8.13.4. Recent initiatives 136 8.14. Silicon Therapeutics 136 8.14.1. Company Overview 136 8.14.2. Silicon Therapeutics Revenue and Gross Margin 136 8.14.3. Product portfolio 137 8.14.4. Recent initiatives 138 8.15. Cloud Pharmaceuticals Inc. 138 8.15.1. Company Overview 138 8.15.2. Cloud Pharmaceuticals Inc. Revenue and Gross Margin 138 8.15.3. Product portfolio 139 8.15.4. Recent initiatives 140 8.16. Novartis Pharmaceuticals Corporation 140 8.16.1. Company Overview 140 8.16.2. Novartis Pharmaceuticals Corporation Revenue and Gross Margin 140 8.16.3. Product portfolio 141 8.16.4. Recent initiatives 142 8.17. Teva Pharmaceutical Industries Ltd. 142 8.17.1. Company Overview 142 8.17.2. Teva Pharmaceutical Industries Ltd. Revenue and Gross Margin 142 8.17.3. Product portfolio 143 8.17.4. Recent initiatives 144 8.18. BenevolentAI 144 8.18.1. Company Overview 144 8.18.2. BenevolentAI Revenue and Gross Margin 144 8.18.3. Product portfolio 145 8.18.4. Recent initiatives 146 8.19. Exscientia 146 8.19.1. Company Overview 146 8.19.2. Exscientia Revenue and Gross Margin 146 8.19.3. Product portfolio 147 8.19.4. Recent initiatives 148 8.20. Numerate 148 8.20.1. Company Overview 148 8.20.2. Numerate Revenue and Gross Margin 148 8.20.3. Product portfolio 149 8.20.4. Recent initiatives 150 8.21. Envisagenics Inc. 150 8.21.1. Company Overview 150 8.21.2. Envisagenics Inc. Revenue and Gross Margin 150 8.21.3. Product portfolio 151 8.21.4. Recent initiatives 152 8.22. Accutar Biotech 152 8.22.1. Company Overview 152 8.22.2. Accutar Biotech Revenue and Gross Margin 152 8.22.3. Product portfolio 153 8.22.4. Recent initiatives 154 CHAPTER 9. AI for Drug Discovery — Industry Analysis 155 9.1. AI for Drug Discovery Market – Key Trends 155 9.1.1. Market Drivers 156 9.1.2. Market Restraints 156 9.1.3. Market Opportunities 157 9.2. Value Chain Analysis 158 9.3. Technology Roadmap and Timeline 159 9.4. AI for Drug Discovery Market – Attractiveness Analysis 160 9.4.1. By Drug Type 160 9.4.2. By Technology 160 9.4.3. By Therapeutic Area 161 9.4.4. By End User 162 9.4.5. By Region 163 CHAPTER 10. Marketing Strategy Analysis, Distributors 164 10.1. Marketing Channel 164 10.2. Direct Marketing 165 10.3. Indirect Marketing 165 10.4. Marketing Channel Development Trend 165 10.5. Economic/Political Environmental Change 166 CHAPTER 11. Report Conclusion 167 CHAPTER 12. Research Approach & Methodology 168 12.1. Report Description 168 12.2. Research Scope 169 12.3. Research Methodology 169 12.3.1. Secondary Research 170 12.3.2. Primary Research 171 12.3.3. Models 172 12.3.3.1. Company Share Analysis Model 172 12.3.3.2. Revenue Based Modeling 173 12.3.3.3. Research Limitations 173
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