Deep Learning in Drug Discovery Market Report and Forecast 2025-2034
Description
The global deep learning in drug discovery market was valued at USD 4.63 Billion in 2024, driven by advancements in artificial intelligence technologies across the globe. The market is anticipated to grow at a CAGR of 21.90% during the forecast period of 2025-2034, with the values likely to reach USD 33.54 Billion by 2034.
Deep Learning in Drug Discovery Market Overview
Deep learning in drug discovery applies advanced AI techniques to analyze complex biological data, identify drug targets, and predict molecule interactions. It boosts efficiency in the process of discovering and optimizing new drugs. The market is witnessing rapid growth, due to advancements in artificial intelligence and machine learning. This technology accelerates drug discovery by analyzing vast datasets, identifying patterns, and predicting drug candidates. Its applications cut across lead discovery, optimization, and repurposing, streamlining research timelines, reducing the costs associated with research, and driving innovation in the pharmaceutical and biotechnology industries.
Deep Learning in Drug Discovery Market Growth Drivers
Advancements in Artificial Intelligence to Elevate the Market Value Significantly
The market is experiencing rapid growth, fueled by significant advancements in artificial intelligence. For instance, in September 2024, the United States Department of Health and Human Services launched the Target project, which uses deep learning and generative AI to speed up antibiotic discovery and tackle antimicrobial resistance. With these technologies, rapid molecule screening and in silico testing can be accelerated, thereby cutting costs and time. It addresses the need for new treatments and transforms the drug discovery market.
Deep Learning in Drug Discovery Market Trends
The market is witnessing several trends and developments to improve the current scenario. Some of the notable trends are as follows:
Integration of Quantum Computing
Quantum computing is increasingly being integrated with deep learning to solve complex drug discovery problems. It offers an improvement in computing abilities, through which the chemical properties and molecular interactions can be simulated faster. This accelerates the drug discovery process and opens the scope for tackling challenging diseases.
Use of Generative AI Models
Generative AI models are being widely used in drug discovery for the design of novel compounds. Given their ability to predict molecular structures with desirable properties, the trial-and-error phase in traditional drug design has been reduced considerably with increased efficiency in drug pipelines.
Collaboration Between AI Companies and Pharma Giants
One of the emerging trends in the market is partnerships between tech companies specializing in AI and pharmaceutical corporations. This combines technological expertise with domain knowledge, leading to significant breakthroughs in drug discovery, repurposing existing drugs, and finding treatments for unmet medical needs.
Application in Rare Disease Drug Discovery
Deep learning is revolutionizing the development of treatments for rare diseases, where limited data is available. By analyzing small datasets and identifying potential targets, AI accelerates progress in this underserved area, bringing new market opportunities.
Deep Learning in Drug Discovery Market Segmentation
The market report offers a detailed analysis of the market based on the following segments:
Market Breakup by Therapeutic Area
Segmentation Based on Therapeutic Area to Witness Substantial Growth
Based on therapeutic area, the market is segmented into oncological disorders, infectious diseases, neurological disorders, immunological disorders, endocrine disorders, cardiovascular disorders, respiratory disorders, eye disorders, musculoskeletal disorders, inflammatory disorders, and others. Oncological disorders are expected to have a substantial share of the market. The high incidence of cancer and the urgency to discover novel therapies drive the adoption of deep learning to search for candidates, optimize treatment, and enhance precision medicine.
Deep Learning in Drug Discovery Market Analysis by Region
The market is divided into regions such as North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. North America is expected to account for a significant share of the market because of its strong pharmaceutical industry, advanced AI infrastructure, and huge investments in R&D. The United States is a key contributor, with initiatives from tech giants and startups transforming the sector. Favorable regulation and strong collaboration between academia and industry also complement the region's dominance in this market.
Leading Players in the Deep Learning in Drug Discovery Market
The key features of the market report comprise patent analysis, grants analysis, funding and investment analysis, and strategic initiatives by the leading players. The major companies in the market are as follows:
Aiforia Technologies Oyj
Aiforia Technologies is one of the leading players in the market. The company was established in 2013 and has headquarters in Helsinki, Finland. Their flagship solutions Aiforia® Create and Aiforia® Studies use AI to transform pathology workflows with automated, GLP-compliant image analysis for preclinical research, accelerating discoveries in drug development.
Ardigen SA
Ardigen, established in 2015, is an AI leader for drug discovery. With headquarters located in Kraków, Poland, the company focuses on products such as phenAID, which is multi-modal and combines phenotypic and chemical data in enhanced predictions of Mode of Action (MoA) and bioactivity, speeding the delivery of novel drugs using phenotypic drug discovery.
Google LLC
Google LLC, through its subsidiaries and platforms, has significantly advanced the global deep learning in drug discovery market. For instance, DeepMind, a subsidiary of Alphabet Inc., introduced AlphaFold, an AI system capable of predicting protein structures with high accuracy. This innovative platform has been instrumental in understanding biological processes and accelerating drug discovery efforts.
Huawei Technologies Co., Ltd
Huawei Technologies Co., Ltd., located in Shenzhen, China, was founded in 1987. The company is a global leader in telecommunications and technology, providing innovative AI solutions. In deep learning drug discovery, Huawei has the leading product Pangu Drug Molecule Model, which utilizes AI technology to predict the effective drug compounds used for disease treatment, saving on research and development costs.
Other key players in the market include Aegicare (Shenzhen) Technology Co., Ltd, Berg LLC, Merative L.P, Nference, Inc., Nvidia Corporation, Owkin Inc., Phenomic AI Inc., Atomwise Inc., BenevolentAI Limited, and XtalPi Inc.
Key Questions Answered in the Deep Learning in Drug Discovery Market Report
Deep Learning in Drug Discovery Market Overview
Deep learning in drug discovery applies advanced AI techniques to analyze complex biological data, identify drug targets, and predict molecule interactions. It boosts efficiency in the process of discovering and optimizing new drugs. The market is witnessing rapid growth, due to advancements in artificial intelligence and machine learning. This technology accelerates drug discovery by analyzing vast datasets, identifying patterns, and predicting drug candidates. Its applications cut across lead discovery, optimization, and repurposing, streamlining research timelines, reducing the costs associated with research, and driving innovation in the pharmaceutical and biotechnology industries.
Deep Learning in Drug Discovery Market Growth Drivers
Advancements in Artificial Intelligence to Elevate the Market Value Significantly
The market is experiencing rapid growth, fueled by significant advancements in artificial intelligence. For instance, in September 2024, the United States Department of Health and Human Services launched the Target project, which uses deep learning and generative AI to speed up antibiotic discovery and tackle antimicrobial resistance. With these technologies, rapid molecule screening and in silico testing can be accelerated, thereby cutting costs and time. It addresses the need for new treatments and transforms the drug discovery market.
Deep Learning in Drug Discovery Market Trends
The market is witnessing several trends and developments to improve the current scenario. Some of the notable trends are as follows:
Integration of Quantum Computing
Quantum computing is increasingly being integrated with deep learning to solve complex drug discovery problems. It offers an improvement in computing abilities, through which the chemical properties and molecular interactions can be simulated faster. This accelerates the drug discovery process and opens the scope for tackling challenging diseases.
Use of Generative AI Models
Generative AI models are being widely used in drug discovery for the design of novel compounds. Given their ability to predict molecular structures with desirable properties, the trial-and-error phase in traditional drug design has been reduced considerably with increased efficiency in drug pipelines.
Collaboration Between AI Companies and Pharma Giants
One of the emerging trends in the market is partnerships between tech companies specializing in AI and pharmaceutical corporations. This combines technological expertise with domain knowledge, leading to significant breakthroughs in drug discovery, repurposing existing drugs, and finding treatments for unmet medical needs.
Application in Rare Disease Drug Discovery
Deep learning is revolutionizing the development of treatments for rare diseases, where limited data is available. By analyzing small datasets and identifying potential targets, AI accelerates progress in this underserved area, bringing new market opportunities.
Deep Learning in Drug Discovery Market Segmentation
The market report offers a detailed analysis of the market based on the following segments:
Market Breakup by Therapeutic Area
- Oncological Disorders
- Infectious Diseases
- Neurological Disorders
- Immunological Disorders
- Endocrine Disorders
- Cardiovascular Disorders
- Respiratory Disorders
- Eye Disorders
- Musculoskeletal Disorders
- Inflammatory Disorders
- Others
- Target Identification and Selection
- Target Identification
- Hit Identification Prioritization
- Lead Optimization
- Candidate Selection and Validation
- Pharmaceutical Companies
- Biotechnology Companies
- Contract Research Organization
- Academic and Research Institute
- Others
- North America
- Europe
- Asia Pacific
- Latin America
- Middle East and Africa
Segmentation Based on Therapeutic Area to Witness Substantial Growth
Based on therapeutic area, the market is segmented into oncological disorders, infectious diseases, neurological disorders, immunological disorders, endocrine disorders, cardiovascular disorders, respiratory disorders, eye disorders, musculoskeletal disorders, inflammatory disorders, and others. Oncological disorders are expected to have a substantial share of the market. The high incidence of cancer and the urgency to discover novel therapies drive the adoption of deep learning to search for candidates, optimize treatment, and enhance precision medicine.
Deep Learning in Drug Discovery Market Analysis by Region
The market is divided into regions such as North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. North America is expected to account for a significant share of the market because of its strong pharmaceutical industry, advanced AI infrastructure, and huge investments in R&D. The United States is a key contributor, with initiatives from tech giants and startups transforming the sector. Favorable regulation and strong collaboration between academia and industry also complement the region's dominance in this market.
Leading Players in the Deep Learning in Drug Discovery Market
The key features of the market report comprise patent analysis, grants analysis, funding and investment analysis, and strategic initiatives by the leading players. The major companies in the market are as follows:
Aiforia Technologies Oyj
Aiforia Technologies is one of the leading players in the market. The company was established in 2013 and has headquarters in Helsinki, Finland. Their flagship solutions Aiforia® Create and Aiforia® Studies use AI to transform pathology workflows with automated, GLP-compliant image analysis for preclinical research, accelerating discoveries in drug development.
Ardigen SA
Ardigen, established in 2015, is an AI leader for drug discovery. With headquarters located in Kraków, Poland, the company focuses on products such as phenAID, which is multi-modal and combines phenotypic and chemical data in enhanced predictions of Mode of Action (MoA) and bioactivity, speeding the delivery of novel drugs using phenotypic drug discovery.
Google LLC
Google LLC, through its subsidiaries and platforms, has significantly advanced the global deep learning in drug discovery market. For instance, DeepMind, a subsidiary of Alphabet Inc., introduced AlphaFold, an AI system capable of predicting protein structures with high accuracy. This innovative platform has been instrumental in understanding biological processes and accelerating drug discovery efforts.
Huawei Technologies Co., Ltd
Huawei Technologies Co., Ltd., located in Shenzhen, China, was founded in 1987. The company is a global leader in telecommunications and technology, providing innovative AI solutions. In deep learning drug discovery, Huawei has the leading product Pangu Drug Molecule Model, which utilizes AI technology to predict the effective drug compounds used for disease treatment, saving on research and development costs.
Other key players in the market include Aegicare (Shenzhen) Technology Co., Ltd, Berg LLC, Merative L.P, Nference, Inc., Nvidia Corporation, Owkin Inc., Phenomic AI Inc., Atomwise Inc., BenevolentAI Limited, and XtalPi Inc.
Key Questions Answered in the Deep Learning in Drug Discovery Market Report
- What was the deep learning in drug discovery market value in 2024?
- What is the deep learning in drug discovery market forecast outlook for 2025-2034?
- What are the regional markets covered in the EMR report?
- What is the market segmentation based on the therapeutic area?
- What is the market breakup by process?
- What is the market breakup based on the end user?
- What major factors aid the deep learning in drug discovery market demand?
- How has the market performed so far and how is it anticipated to perform in the coming years?
- What are the major drivers, opportunities, and restraints in the market?
- What are the major trends influencing the market?
- Which regional market is expected to dominate the market share in the forecast period?
- Which country is likely to experience elevated growth during the forecast period?
- Who are the key players involved in the deep learning in drug discovery market?
- What are the current unmet needs and challenges in the market?
- How are partnerships, collaborations, mergers, and acquisitions among the key market players shaping the market dynamics?
Table of Contents
400 Pages
- 1 Preface
- 1.1 Objectives of the Study
- 1.2 Key Assumptions
- 1.3 Report Coverage – Key Segmentation and Scope
- 1.4 Research Methodology
- 2 Executive Summary
- 3 Global Deep Learning in Drug Discovery Market Overview
- 3.1 Global Deep Learning in Drug Discovery Market Historical Value (2018-2024)
- 3.2 Global Deep Learning in Drug Discovery Market Forecast Value (2025-2034)
- 4 Vendor Positioning Analysis
- 4.1 Key Vendors
- 4.2 Prospective Leaders
- 4.3 Niche Leaders
- 4.4 Disruptors
- 5 Global Deep Learning in Drug Discovery Market Landscape*
- 5.1 Global Deep Learning in Drug Discovery Market: Developers Landscape
- 5.1.1 Analysis by Year of Establishment
- 5.1.2 Analysis by Company Size
- 5.1.3 Analysis by Region
- 5.2 Global Deep Learning in Drug Discovery Market: Product Landscape
- 5.2.1 Analysis by Therapeutic Area
- 5.2.2 Analysis by Process
- 6 Global Deep Learning in Drug Discovery Market Dynamics
- 6.1 Market Drivers and Constraints
- 6.2 SWOT Analysis
- 6.2.1 Strengths
- 6.2.2 Weaknesses
- 6.2.3 Opportunities
- 6.2.4 Threats
- 6.3 PESTEL Analysis
- 6.3.1 Political
- 6.3.2 Economic
- 6.3.3 Social
- 6.3.4 Technological
- 6.3.5 Legal
- 6.3.6 Environment
- 6.4 Porter’s Five Forces Model
- 6.4.1 Bargaining Power of Suppliers
- 6.4.2 Bargaining Power of Buyers
- 6.4.3 Threat of New Entrants
- 6.4.4 Threat of Substitutes
- 6.4.5 Degree of Rivalry
- 6.5 Key Demand Indicators
- 6.6 Key Price Indicators
- 6.7 Industry Events, Initiatives, and Trends
- 6.8 Value Chain Analysis
- 7 Global Deep Learning in Drug Discovery Market Segmentation (218-2034)
- 7.1 Global Deep Learning in Drug Discovery Market (2018-2034) by Therapeutic Area
- 7.1.1 Market Overview
- 7.1.2 Oncological Disorders
- 7.1.3 Infectious Diseases
- 7.1.4 Neurological Disorders
- 7.1.5 Immunological Disorders
- 7.1.6 Endocrine Disorders
- 7.1.7 Cardiovascular Disorders
- 7.1.8 Respiratory Disorders
- 7.1.9 Eye Disorders
- 7.1.10 Musculoskeletal Disorders
- 7.1.11 Inflammatory Disorders
- 7.1.12 Others
- 7.2 Global Deep Learning in Drug Discovery Market (2018-2034) by Process
- 7.2.1 Market Overview
- 7.2.2 Target Identification and Selection
- 7.2.3 Target Identification
- 7.2.4 Hit Identification Prioritization
- 7.2.5 Lead Optimization
- 7.2.6 Candidate Selection and Validation
- 7.3 Global Deep Learning in Drug Discovery Market (2018-2034) by End User
- 7.3.1 Market Overview
- 7.3.2 Pharmaceutical Companies
- 7.3.3 Biotechnology Companies
- 7.3.4 Contract Research Organization
- 7.3.5 Academic and Research Institute
- 7.3.6 Others
- 7.4 Global Deep Learning in Drug Discovery Market (2018-2034) by Region
- 7.4.1 Market Overview
- 7.4.2 North America
- 7.4.3 Europe
- 7.4.4 Asia Pacific
- 7.4.5 Latin America
- 7.4.6 Middle East and Africa
- 8 North America Deep Learning in Drug Discovery Market (218-2034)
- 8.1 North America Deep Learning in Drug Discovery Market (2018-2034) by Therapeutic Area
- 8.1.1 Market Overview
- 8.1.2 Oncological Disorders
- 8.1.3 Infectious Diseases
- 8.1.4 Neurological Disorders
- 8.1.5 Immunological Disorders
- 8.1.6 Endocrine Disorders
- 8.1.7 Cardiovascular Disorders
- 8.1.8 Respiratory Disorders
- 8.1.9 Eye Disorders
- 8.1.10 Musculoskeletal Disorders
- 8.1.11 Inflammatory Disorders
- 8.1.12 Others
- 8.2 North America Deep Learning in Drug Discovery Market (2018-2034) by Process
- 8.2.1 Market Overview
- 8.2.2 Target Identification and Selection
- 8.2.3 Target Identification
- 8.2.4 Hit Identification Prioritization
- 8.2.5 Lead Optimization
- 8.2.6 Candidate Selection and Validation
- 8.3 North America Deep Learning in Drug Discovery Market (2018-2034) by End User
- 8.3.1 Market Overview
- 8.3.2 Pharmaceutical Companies
- 8.3.3 Biotechnology Companies
- 8.3.4 Contract Research Organization
- 8.3.5 Academic and Research Institute
- 8.3.6 Others
- 8.4 North America Deep Learning in Drug Discovery Market (2018-2034) by Country
- 8.4.1 United States of America
- 8.4.2 Canada
- 9 Europe Deep Learning in Drug Discovery Market (218-2034)
- 9.1 Europe Deep Learning in Drug Discovery Market (2018-2034) by Therapeutic Area
- 9.1.1 Market Overview
- 9.1.2 Oncological Disorders
- 9.1.3 Infectious Diseases
- 9.1.4 Neurological Disorders
- 9.1.5 Immunological Disorders
- 9.1.6 Endocrine Disorders
- 9.1.7 Cardiovascular Disorders
- 9.1.8 Respiratory Disorders
- 9.1.9 Eye Disorders
- 9.1.10 Musculoskeletal Disorders
- 9.1.11 Inflammatory Disorders
- 9.1.12 Others
- 9.2 Europe Deep Learning in Drug Discovery Market (2018-2034) by Process
- 9.2.1 Market Overview
- 9.2.2 Target Identification and Selection
- 9.2.3 Target Identification
- 9.2.4 Hit Identification Prioritization
- 9.2.5 Lead Optimization
- 9.2.6 Candidate Selection and Validation
- 9.3 Europe Deep Learning in Drug Discovery Market (2018-2034) by End User
- 9.3.1 Market Overview
- 9.3.2 Pharmaceutical Companies
- 9.3.3 Biotechnology Companies
- 9.3.4 Contract Research Organization
- 9.3.5 Academic and Research Institute
- 9.3.6 Others
- 9.4 Europe Deep Learning in Drug Discovery Market (2018-2034) by Country
- 9.4.1 United Kingdom
- 9.4.2 Germany
- 9.4.3 France
- 9.4.4 Italy
- 9.4.5 Others
- 10 Asia Pacific Deep Learning in Drug Discovery Market (218-2034)
- 10.1 Asia Pacific Deep Learning in Drug Discovery Market (2018-2034) by Therapeutic Area
- 10.1.1 Market Overview
- 10.1.2 Oncological Disorders
- 10.1.3 Infectious Diseases
- 10.1.4 Neurological Disorders
- 10.1.5 Immunological Disorders
- 10.1.6 Endocrine Disorders
- 10.1.7 Cardiovascular Disorders
- 10.1.8 Respiratory Disorders
- 10.1.9 Eye Disorders
- 10.1.10 Musculoskeletal Disorders
- 10.1.11 Inflammatory Disorders
- 10.1.12 Others
- 10.2 Asia Pacific Deep Learning in Drug Discovery Market (2018-2034) by Process
- 10.2.1 Market Overview
- 10.2.2 Target Identification and Selection
- 10.2.3 Target Identification
- 10.2.4 Hit Identification Prioritization
- 10.2.5 Lead Optimization
- 10.2.6 Candidate Selection and Validation
- 10.3 Asia Pacific Deep Learning in Drug Discovery Market (2018-2034) by End User
- 10.3.1 Market Overview
- 10.3.2 Pharmaceutical Companies
- 10.3.3 Biotechnology Companies
- 10.3.4 Contract Research Organization
- 10.3.5 Academic and Research Institute
- 10.3.6 Others
- 10.4 Asia Pacific Deep Learning in Drug Discovery Market (2018-2034) by Country
- 10.4.1 China
- 10.4.2 Japan
- 10.4.3 India
- 10.4.4 ASEAN
- 10.4.5 Australia
- 10.4.6 Others
- 11 Latin America Deep Learning in Drug Discovery Market (218-2034)
- 11.1 Latin America Deep Learning in Drug Discovery Market (2018-2034) by Therapeutic Area
- 11.1.1 Market Overview
- 11.1.2 Oncological Disorders
- 11.1.3 Infectious Diseases
- 11.1.4 Neurological Disorders
- 11.1.5 Immunological Disorders
- 11.1.6 Endocrine Disorders
- 11.1.7 Cardiovascular Disorders
- 11.1.8 Respiratory Disorders
- 11.1.9 Eye Disorders
- 11.1.10 Musculoskeletal Disorders
- 11.1.11 Inflammatory Disorders
- 11.1.12 Others
- 11.2 Latin America Deep Learning in Drug Discovery Market (2018-2034) by Process
- 11.2.1 Market Overview
- 11.2.2 Target Identification and Selection
- 11.2.3 Target Identification
- 11.2.4 Hit Identification Prioritization
- 11.2.5 Lead Optimization
- 11.2.6 Candidate Selection and Validation
- 11.3 Latin America Deep Learning in Drug Discovery Market (2018-2034) by End User
- 11.3.1 Market Overview
- 11.3.2 Pharmaceutical Companies
- 11.3.3 Biotechnology Companies
- 11.3.4 Contract Research Organization
- 11.3.5 Academic and Research Institute
- 11.3.6 Others
- 11.4 Latin America Deep Learning in Drug Discovery Market (2018-2034) by Country
- 11.4.1 Brazil
- 11.4.2 Argentina
- 11.4.3 Mexico
- 11.4.4 Others
- 12 Middle East and Africa Deep Learning in Drug Discovery Market (218-2034)
- 12.1 Middle East and Africa Deep Learning in Drug Discovery Market (2018-2034) by Therapeutic Area
- 12.1.1 Market Overview
- 12.1.2 Oncological Disorders
- 12.1.3 Infectious Diseases
- 12.1.4 Neurological Disorders
- 12.1.5 Immunological Disorders
- 12.1.6 Endocrine Disorders
- 12.1.7 Cardiovascular Disorders
- 12.1.8 Respiratory Disorders
- 12.1.9 Eye Disorders
- 12.1.10 Musculoskeletal Disorders
- 12.1.11 Inflammatory Disorders
- 12.1.12 Others
- 12.2 Middle East and Africa Deep Learning in Drug Discovery Market (2018-2034) by Process
- 12.2.1 Market Overview
- 12.2.2 Target Identification and Selection
- 12.2.3 Target Identification
- 12.2.4 Hit Identification Prioritization
- 12.2.5 Lead Optimization
- 12.2.6 Candidate Selection and Validation
- 12.3 Middle East and Africa Deep Learning in Drug Discovery Market (2018-2034) by End User
- 12.3.1 Market Overview
- 12.3.2 Pharmaceutical Companies
- 12.3.3 Biotechnology Companies
- 12.3.4 Contract Research Organization
- 12.3.5 Academic and Research Institute
- 12.3.6 Others
- 12.4 Middle East and Africa Deep Learning in Drug Discovery Market (2018-2034) by Country
- 12.4.1 Saudi Arabia
- 12.4.2 United Arab Emirates
- 12.4.3 Nigeria
- 12.4.4 South Africa
- 12.4.5 Others
- 13 Patent Analysis
- 13.1 Analysis by Type of Patent
- 13.2 Analysis by Publication Year
- 13.3 Analysis by Issuing Authority
- 13.4 Analysis by Patent Age
- 13.5 Analysis by CPC Analysis
- 13.6 Analysis by Patent Valuation
- 14 Grants Analysis
- 14.1 Analysis by Year
- 14.2 Analysis by Amount Awarded
- 14.3 Analysis by Issuing Authority
- 14.4 Analysis by Grant Application
- 14.5 Analysis by Funding Institute
- 14.6 Analysis by NIH Departments
- 14.7 Analysis by Recipient Organization
- 15 Funding and Investment Analysis
- 15.1 Analysis by Funding Instances
- 15.2 Analysis by Drug Class of Funding
- 15.3 Analysis by Funding Amount
- 15.4 Analysis by Leading Players
- 15.5 Analysis by Leading Investors
- 15.6 Analysis by Geography
- 16 Strategic Initiatives
- 16.1 Analysis by Partnership Instances
- 16.2 Analysis by Drug Class of Partnership
- 16.3 Analysis by Leading Players
- 16.4 Analysis by Geography
- 17 Supplier Landscape
- 17.1 Market Share Analysis, By Region (Top 5 Companies)
- 17.1.1 Market Share Analysis: Global
- 17.1.2 Market Share Analysis: North America
- 17.1.3 Market Share Analysis: Europe
- 17.1.4 Market Share Analysis: Asia Pacific
- 17.1.5 Market Share Analysis: Others
- 17.2 Aegicare (Shenzhen) Technology Co., Ltd
- 17.2.1 Financial Analysis
- 17.2.2 Product Portfolio
- 17.2.3 Demographic Reach and Achievements
- 17.2.4 Company News and Development
- 17.2.5 Certifications
- 17.3 Aiforia Technologies Oyj
- 17.3.1 Financial Analysis
- 17.3.2 Product Portfolio
- 17.3.3 Demographic Reach and Achievements
- 17.3.4 Company News and Development
- 17.3.5 Certifications
- 17.4 Ardigen SA
- 17.4.1 Financial Analysis
- 17.4.2 Product Portfolio
- 17.4.3 Demographic Reach and Achievements
- 17.4.4 Company News and Development
- 17.4.5 Certifications
- 17.5 Berg LLC
- 17.5.1 Financial Analysis
- 17.5.2 Product Portfolio
- 17.5.3 Demographic Reach and Achievements
- 17.5.4 Company News and Development
- 17.5.5 Certifications
- 17.6 Google LLC
- 17.6.1 Financial Analysis
- 17.6.2 Product Portfolio
- 17.6.3 Demographic Reach and Achievements
- 17.6.4 Company News and Development
- 17.6.5 Certifications
- 17.7 Huawei Technologies Co., Ltd
- 17.7.1 Financial Analysis
- 17.7.2 Product Portfolio
- 17.7.3 Demographic Reach and Achievements
- 17.7.4 Company News and Development
- 17.7.5 Certifications
- 17.8 Merative L.P
- 17.8.1 Financial Analysis
- 17.8.2 Product Portfolio
- 17.8.3 Demographic Reach and Achievements
- 17.8.4 Company News and Development
- 17.8.5 Certifications
- 17.9 Nference, Inc
- 17.9.1 Financial Analysis
- 17.9.2 Product Portfolio
- 17.9.3 Demographic Reach and Achievements
- 17.9.4 Company News and Development
- 17.9.5 Certifications
- 17.10 Nvidia Corporation
- 17.10.1 Financial Analysis
- 17.10.2 Product Portfolio
- 17.10.3 Demographic Reach and Achievements
- 17.10.4 Company News and Development
- 17.10.5 Certifications
- 17.11 Owkin Inc.
- 17.11.1 Financial Analysis
- 17.11.2 Product Portfolio
- 17.11.3 Demographic Reach and Achievements
- 17.11.4 Company News and Development
- 17.11.5 Certifications
- 17.12 Phenomic AI Inc.
- 17.12.1 Financial Analysis
- 17.12.2 Product Portfolio
- 17.12.3 Demographic Reach and Achievements
- 17.12.4 Company News and Development
- 17.12.5 Certifications
- 17.13 Atomwise Inc
- 17.13.1 Financial Analysis
- 17.13.2 Product Portfolio
- 17.13.3 Demographic Reach and Achievements
- 17.13.4 Company News and Development
- 17.13.5 Certifications
- 17.14 BenevolentAI Limited
- 17.14.1 Financial Analysis
- 17.14.2 Product Portfolio
- 17.14.3 Demographic Reach and Achievements
- 17.14.4 Company News and Development
- 17.14.5 Certifications
- 17.15 XtalPi Inc
- 17.15.1 Financial Analysis
- 17.15.2 Product Portfolio
- 17.15.3 Demographic Reach and Achievements
- 17.15.4 Company News and Development
- 17.15.5 Certifications
- 18 Global Deep Learning in Drug Discovery Market – Distribution Model (Additional Insight)
- 18.1 Overview
- 18.2 Potential Distributors
- 18.3 Key Parameters for Distribution Partner Assessment
- 19 Key Opinion Leaders (KOL) Insights (Additional Insight)
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