AI in Drug Discovery Market
Description
AI in Drug Discovery Market Analysis and Forecast to 2035: Type, Services, Technology, Application, End User, Deployment, Offering, Therapeutic AreaAI in Drug Discovery Market is anticipated to expand from $2.8 billion in 2025 to $14.0 billion by 2035, growing at a CAGR of approximately 16.1%. The AI-driven drug discovery market in 2025 is witnessing rapid growth, fueled by strong government support, strategic collaborations, innovative startups, and increasing global adoption. Governments are actively promoting AI integration in drug development: China has prioritized AI drug discovery in its 2025 Five-Year Plan, with funding from local pharmaceutical hubs such as Shanghai supporting biotech ventures; the U.S. Department of Health and Human Services (HHS) released a strategic plan to enhance efficiency and innovation in health services, including drug discovery; and the UK's MHRA introduced the "AI Airlock," a regulatory sandbox facilitating safe development and approval of AI-driven medical devices and diagnostics.
Industry collaborations are also driving market momentum, exemplified by XtalPi and DoveTree's $6 billion partnership targeting oncology, immunology, neurological, and metabolic diseases, Almirall and Absci expanding AI drug creation to dermatology, and Serviers extended collaboration with Google Cloud to leverage AI and generative AI in R&D.
Startups are increasingly contributing to innovation, with AMPLY Discovery securing $1.75 million to develop therapies for aggressive cancers, Atomic AI raising $42 million to advance RNA-based therapeutics, and Israels AION Labs emerging as a key player in pharmaceutical AI.
The market is supported by flexible pricing models, including annual license fees ranging from $500,000 to several million dollars, tiered access, and volume-based pricing, allowing companies to balance AI infrastructure costs with scalable adoption. Investment trends remain robust, with venture capital funding rebounding in 2024, Ignota Labs reducing drug development timelines to under two years at costs below $1 million, and AMD investing $20 million in Absci to support biologics innovation.
Regionally, China is experiencing a surge in domestic AI-driven drug discovery as companies like Shanghai Titan Scientific and Nanjing Vazyme Biotech capitalize on local reagent supply, while Chinese firms accounted for 32% of global biotech licensing deal value in Q1 2025. In India, market growth is driven by rising chronic disease prevalence and adoption of AI and ML tools by pharmaceutical and biotechnology companies. Meanwhile, the UKs 108 billion biotech sector faces pricing and investment challenges despite government initiatives such as a 600 million Health Data Research Service and a 520 million manufacturing investment, prompting some firms to explore opportunities abroad. Hence, the AI drug discovery market is characterized by accelerating innovation, substantial investments, and increasing global adoption across both established and emerging pharmaceutical hubs.
Segment Overview
Based on technology, the AI in drug discovery market is segmented into Machine Learning, Computer Vision, Context Awareness Computing, Natural Language Processing. In 2024, Machine Learning (ML) accounted for 35.9% of the AI in drug discovery market, highlighting its ability to process both structured and unstructured biomedical data efficiently. Traditional ML methods, such as gradient-boosted trees, are useful for straightforward tasks, while advanced approaches like graph neural networks help identify complex patterns in protein-ligand interactions. The growth of this segment is driven by the increasing use of ML in key stages of drug discovery, including target identification and preclinical testing. For example, BenevolentAI uses ML to analyze large datasets, allowing researchers to find potential drug candidates faster and more accurately. In February 2025, Merck expanded its internal generative AI platform, built on Amazon SageMaker, to accelerate clinical study report production and streamline decision-making in drug development. The platform uses AWS DataSync to efficiently ingest and process large datasets, reducing time and costs in critical-path clinical activities. ML is also applied in bioinformatics to discover new biomarkers and therapeutic targets, supporting personalized medicine approaches. In virtual screening, ML helps researchers quickly evaluate large chemical libraries to identify promising compounds. Cloud-based ML platforms, such as Google Cloud AI, further support collaboration by providing scalable computing power and shared data resources, enabling faster and more efficient drug development.
The application segment includes various areas such as Drug Optimization and Repurposing, Preclinical Testing, Target Identification, Others. Among these, the preclinical testing segment is expected to grow at a CAGR of 21.1% from 2025 to 2034, driven by the increasing use of AI to improve early-stage drug development. Preclinical testing involves evaluating drug candidates for safety, efficacy, and toxicity before human trials, a process that is traditionally time-consuming and expensive. AI technologies, including machine learning, deep learning, and computer vision, are being leveraged to simulate biological processes, analyze large datasets, and predict drug behavior more efficiently. In June 2025, MIT and Recursion Pharmaceuticals released the Boltz-2 AI model, a next-generation system that achieves best-in-class accuracy in modeling complex molecular structures and predicting binding affinities, enhancing preclinical candidate selection. In July 2025, Tahoe Therapeutics secured $30 million in funding, led by Amplify Partners, to develop AI-driven human cell models. The initiative aims to generate one billion single-cell data points and map one million drug-patient interactions, accelerating safety and efficacy testing in preclinical research. These advancements are reducing timelines and costs, making preclinical testing the fastest-growing application segment in AI-driven drug discovery.
Geographical Overview
In 2024, North America accounted for the largest share of the AI in drug discovery market, holding 53.5% of the global market. This growth is driven by the increasing demand for innovative drug development processes and the integration of AI in research and clinical trials. In February 2024, Ginkgo Bioworks acquired Reverie Labs AI/ML tools to strengthen AI-driven discovery services and develop next-generation biological foundation models. Similarly, in November 2023, Brainomix, specializing in AI-powered software for precision medicine, expanded into the U.S., while in May 2023, Google introduced AI solutions to streamline drug discovery and precision medicine for biotech and pharmaceutical firms. Academic contributions, such as Stanford Universitys Artificial Intelligence for Structure-Based Drug Discovery program, are enhancing safe and effective drug design. Supportive regulatory frameworks, including the FDAs AI/ML-Based Software as a Medical Device (SaMD) Action Plan, are fostering innovation while ensuring safety and efficacy. Additionally, U.S. funding for drug discovery and biotechnology reached 72% of total biotech investment in 2022, further driving market expansion and AI adoption across the region.
The Asia-Pacific region is expected to be the fastest-growing market for AI in drug discovery, with a projected CAGR of 24.9% from 2025 to 2034. Growth is driven by rising healthcare demand, increasing R&D investments, and adoption of AI technologies to accelerate drug discovery and clinical trials. China is rapidly emerging as a powerhouse, filing thousands of AI-driven drug discovery patents and accounting for 32% of global biotech licensing deal value in Q1 2025, up from 21% in 2023 and 2024. Strong government backing, large investments, and access to extensive population data are fueling this surge. In India, the Department of Biotechnology (DBT) and BIRAC actively promote AI, organizing Bio-AI workshops and issuing proposals under the BioE3 policy in 2025 to advance high-performance biomanufacturing and AI applications. Companies like Fujitsu and RIKEN collaborated in 2023 to develop AI-driven technology integrating generative AI with electron microscope imagery for protein structure prediction. Additionally, Japanese companies are investing in AI to enhance drug discovery and personalized therapies, with startups like CytoReason entering Japan through partnerships with Summit Pharmaceuticals. These developments position Asia-Pacific as a rapidly evolving hub for AI-powered drug discovery.
Key Trends and Drivers
Growing Focus on Collaborations & Partnerships -
The adoption of AI in drug discovery is rapidly accelerating, driven by the increasing recognition of its potential across pharmaceutical and biotechnology sectors. Companies are integrating AI not only in drug discovery but also in clinical development, safety monitoring, and risk assessment. To strengthen their competitive position, major players are increasingly forming strategic partnerships and collaborations with AI technology providers, enabling access to advanced AI-driven platforms and fostering innovation. For example, in September 2023, Merck partnered with BenevolentAI UK to leverage its sophisticated AI platform, collaborating with multidisciplinary experts to identify promising preclinical candidates and develop novel compounds. Similarly, Intelligent OMICS Ltd joined forces with Janssen Research & Development, LLC to co-develop Intellomxs AI platform, combining multi-omics data with deep learning to reveal new disease mechanisms and therapeutic opportunities. In January 2023, BioNTech acquired InstaDeep for $440 million, significantly enhancing its AI-powered drug discovery capabilities. This acquisition brought in approximately 290 professionals specializing in AI, machine learning, bioengineering, data science, and software development, strengthening BioNTechs capacity to drive innovation and accelerate the development of next-generation therapeutics.
Growth of AI-Powered Drug Discovery Startups -
Startups developing AI-powered solutions are driving significant growth in the drug discovery sector. By leveraging artificial intelligence and machine learning, these companies accelerate drug development, cut costs, and improve the precision of identifying promising drug candidates. AI integration with biological data helps streamline processes like target identification, molecule screening, and clinical trial optimization. Key players include Insilico Medicine, which uses AI to design and optimize novel drug candidates, Exscientia, employing AI-driven automation for personalized medicines and higher clinical trial success, and Atomwise, which applies deep learning to efficiently predict drug interactions. These startups attract substantial investments from venture capitalists and pharmaceutical companies, fostering innovation and collaboration. Notable examples include Xaira Therapeutics, which received $1 million in funding in April 2024, and BioNTechs acquisition of AI-driven startup InstaDeep for $440 million in 2023. Through partnerships and technological advancements, AI-powered startups are transforming drug discovery, improving efficiency, reducing costs, and enhancing patient outcomes.
Biotech Firms Driving Growth Through AI-Powered Drug Discovery -
The biotechnology industry is experiencing significant growth, creating major opportunities for AI integration in drug discovery. The United States remains the global hub, hosting over 2,000 private and public companies, while Indias biotechnology sector has rapidly expanded, contributing 4.25% to GDP and projected to reach $150 billion by 2025 and $270300 billion by 2030. According to IBISWorld, 3,429 biotech businesses were operating in the U.S. as of 2023. Biotechnology drives innovation in product development, particularly in the biopharmaceutical industry, with biopharmaceuticals being the fastest-growing segment due to their efficacy and ability to treat previously untreatable diseases. In 2025, companies such as Exscientia, Atomwise, Recursion Pharmaceuticals, Chai Discovery, Antiverse, Relay Therapeutics, Xaira, LTZ Therapeutics, and Mount Sinai are leveraging AI to design molecules, predict molecular structures, and develop therapies for cancer, rare diseases, and immuno-oncology. These AI-driven innovations streamline drug development, reduce timelines, and unlock new treatment possibilities, positioning the biotechnology sector for transformative advancements globally and in India.
RECENT DEVELOPMENTS
In August 2025, XtalPi and DoveTree announced a $6 billion collaboration to co-develop new therapies. The partnership leverages AI models to predict drug-target interactions. It aims to accelerate research pipelines while reducing R&D costs. The initiative positions both companies as leaders in AI-powered pharmaceutical innovation.
In August 2025, Relay Therapeutics advanced its lead candidate RLY-2608 through clinical trials. The development uses AI to guide precision oncology drug design. Machine learning algorithms optimized molecular interactions for efficacy. This milestone demonstrates AIs impact on accelerating cancer therapy development.
In August 2025, 65LAB and Duke-NUS were awarded $1.5 million to develop an AI-driven platform for antifibrotic drug discovery. The platform targets lung and kidney diseases using machine learning algorithms. It integrates biological and chemical datasets to optimize candidate selection. The grant supports research to accelerate precision medicine development.
In July 2025, Tahoe Therapeutics secured $30 million to advance its AI-powered virtual cell models. The models simulate cellular behavior for improved drug discovery predictions. Funding will support expansion of their AI platform for preclinical research. This initiative aims to reduce reliance on traditional laboratory experiments.
In July 2025, Isomorphic Labs (Alphabet/DeepMind) announced readiness to begin human trials for its first AI-designed drugs. These drugs were developed using DeepMinds structure-predicting AI models. The approach predicts protein-ligand interactions to optimize drug efficacy. This milestone represents a major advancement in AI-driven drug discovery.
In June 2025, NVIDIA and Novo Nordisk partnered to utilize the Gefion AI supercomputer for drug discovery. The collaboration focuses on developing customized AI models for pharmaceutical research. It enables high-speed simulations of molecular interactions and protein folding. The partnership aims to accelerate the identification of promising drug candidates.
In June 2025, Merck introduced an internal generative AI platform to streamline clinical study report development. The platform is designed to speed up the delivery of medicines to patients. It uses AI to process and analyze complex clinical trial data efficiently. This innovation supports faster decision-making in drug development pipelines.
In June 2025, MIT and Recursion Pharmaceuticals launched the Boltz-2 AI model, designed to accurately predict small-molecule binding affinities. This model enhances early-stage drug discovery by improving target identification. It integrates advanced machine learning algorithms with biological datasets. The collaboration aims to accelerate preclinical research and reduce development costs.
KEY PLAYERS
Insilico Medicine, Exscientia, Atomwise, Benevolent AI, Schrodinger, Recursion Pharmaceuticals, Cyclica, Deep Genomics, Xtal Pi, Bio Symetrics, Cloud Pharmaceuticals, Numerate, Two XAR, Valo Health, Silicon Therapeutics, Bench Sci, Healx, Aria Pharmaceuticals, Peptone, and Molecular AI
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Industry collaborations are also driving market momentum, exemplified by XtalPi and DoveTree's $6 billion partnership targeting oncology, immunology, neurological, and metabolic diseases, Almirall and Absci expanding AI drug creation to dermatology, and Serviers extended collaboration with Google Cloud to leverage AI and generative AI in R&D.
Startups are increasingly contributing to innovation, with AMPLY Discovery securing $1.75 million to develop therapies for aggressive cancers, Atomic AI raising $42 million to advance RNA-based therapeutics, and Israels AION Labs emerging as a key player in pharmaceutical AI.
The market is supported by flexible pricing models, including annual license fees ranging from $500,000 to several million dollars, tiered access, and volume-based pricing, allowing companies to balance AI infrastructure costs with scalable adoption. Investment trends remain robust, with venture capital funding rebounding in 2024, Ignota Labs reducing drug development timelines to under two years at costs below $1 million, and AMD investing $20 million in Absci to support biologics innovation.
Regionally, China is experiencing a surge in domestic AI-driven drug discovery as companies like Shanghai Titan Scientific and Nanjing Vazyme Biotech capitalize on local reagent supply, while Chinese firms accounted for 32% of global biotech licensing deal value in Q1 2025. In India, market growth is driven by rising chronic disease prevalence and adoption of AI and ML tools by pharmaceutical and biotechnology companies. Meanwhile, the UKs 108 billion biotech sector faces pricing and investment challenges despite government initiatives such as a 600 million Health Data Research Service and a 520 million manufacturing investment, prompting some firms to explore opportunities abroad. Hence, the AI drug discovery market is characterized by accelerating innovation, substantial investments, and increasing global adoption across both established and emerging pharmaceutical hubs.
Segment Overview
Based on technology, the AI in drug discovery market is segmented into Machine Learning, Computer Vision, Context Awareness Computing, Natural Language Processing. In 2024, Machine Learning (ML) accounted for 35.9% of the AI in drug discovery market, highlighting its ability to process both structured and unstructured biomedical data efficiently. Traditional ML methods, such as gradient-boosted trees, are useful for straightforward tasks, while advanced approaches like graph neural networks help identify complex patterns in protein-ligand interactions. The growth of this segment is driven by the increasing use of ML in key stages of drug discovery, including target identification and preclinical testing. For example, BenevolentAI uses ML to analyze large datasets, allowing researchers to find potential drug candidates faster and more accurately. In February 2025, Merck expanded its internal generative AI platform, built on Amazon SageMaker, to accelerate clinical study report production and streamline decision-making in drug development. The platform uses AWS DataSync to efficiently ingest and process large datasets, reducing time and costs in critical-path clinical activities. ML is also applied in bioinformatics to discover new biomarkers and therapeutic targets, supporting personalized medicine approaches. In virtual screening, ML helps researchers quickly evaluate large chemical libraries to identify promising compounds. Cloud-based ML platforms, such as Google Cloud AI, further support collaboration by providing scalable computing power and shared data resources, enabling faster and more efficient drug development.
The application segment includes various areas such as Drug Optimization and Repurposing, Preclinical Testing, Target Identification, Others. Among these, the preclinical testing segment is expected to grow at a CAGR of 21.1% from 2025 to 2034, driven by the increasing use of AI to improve early-stage drug development. Preclinical testing involves evaluating drug candidates for safety, efficacy, and toxicity before human trials, a process that is traditionally time-consuming and expensive. AI technologies, including machine learning, deep learning, and computer vision, are being leveraged to simulate biological processes, analyze large datasets, and predict drug behavior more efficiently. In June 2025, MIT and Recursion Pharmaceuticals released the Boltz-2 AI model, a next-generation system that achieves best-in-class accuracy in modeling complex molecular structures and predicting binding affinities, enhancing preclinical candidate selection. In July 2025, Tahoe Therapeutics secured $30 million in funding, led by Amplify Partners, to develop AI-driven human cell models. The initiative aims to generate one billion single-cell data points and map one million drug-patient interactions, accelerating safety and efficacy testing in preclinical research. These advancements are reducing timelines and costs, making preclinical testing the fastest-growing application segment in AI-driven drug discovery.
Geographical Overview
In 2024, North America accounted for the largest share of the AI in drug discovery market, holding 53.5% of the global market. This growth is driven by the increasing demand for innovative drug development processes and the integration of AI in research and clinical trials. In February 2024, Ginkgo Bioworks acquired Reverie Labs AI/ML tools to strengthen AI-driven discovery services and develop next-generation biological foundation models. Similarly, in November 2023, Brainomix, specializing in AI-powered software for precision medicine, expanded into the U.S., while in May 2023, Google introduced AI solutions to streamline drug discovery and precision medicine for biotech and pharmaceutical firms. Academic contributions, such as Stanford Universitys Artificial Intelligence for Structure-Based Drug Discovery program, are enhancing safe and effective drug design. Supportive regulatory frameworks, including the FDAs AI/ML-Based Software as a Medical Device (SaMD) Action Plan, are fostering innovation while ensuring safety and efficacy. Additionally, U.S. funding for drug discovery and biotechnology reached 72% of total biotech investment in 2022, further driving market expansion and AI adoption across the region.
The Asia-Pacific region is expected to be the fastest-growing market for AI in drug discovery, with a projected CAGR of 24.9% from 2025 to 2034. Growth is driven by rising healthcare demand, increasing R&D investments, and adoption of AI technologies to accelerate drug discovery and clinical trials. China is rapidly emerging as a powerhouse, filing thousands of AI-driven drug discovery patents and accounting for 32% of global biotech licensing deal value in Q1 2025, up from 21% in 2023 and 2024. Strong government backing, large investments, and access to extensive population data are fueling this surge. In India, the Department of Biotechnology (DBT) and BIRAC actively promote AI, organizing Bio-AI workshops and issuing proposals under the BioE3 policy in 2025 to advance high-performance biomanufacturing and AI applications. Companies like Fujitsu and RIKEN collaborated in 2023 to develop AI-driven technology integrating generative AI with electron microscope imagery for protein structure prediction. Additionally, Japanese companies are investing in AI to enhance drug discovery and personalized therapies, with startups like CytoReason entering Japan through partnerships with Summit Pharmaceuticals. These developments position Asia-Pacific as a rapidly evolving hub for AI-powered drug discovery.
Key Trends and Drivers
Growing Focus on Collaborations & Partnerships -
The adoption of AI in drug discovery is rapidly accelerating, driven by the increasing recognition of its potential across pharmaceutical and biotechnology sectors. Companies are integrating AI not only in drug discovery but also in clinical development, safety monitoring, and risk assessment. To strengthen their competitive position, major players are increasingly forming strategic partnerships and collaborations with AI technology providers, enabling access to advanced AI-driven platforms and fostering innovation. For example, in September 2023, Merck partnered with BenevolentAI UK to leverage its sophisticated AI platform, collaborating with multidisciplinary experts to identify promising preclinical candidates and develop novel compounds. Similarly, Intelligent OMICS Ltd joined forces with Janssen Research & Development, LLC to co-develop Intellomxs AI platform, combining multi-omics data with deep learning to reveal new disease mechanisms and therapeutic opportunities. In January 2023, BioNTech acquired InstaDeep for $440 million, significantly enhancing its AI-powered drug discovery capabilities. This acquisition brought in approximately 290 professionals specializing in AI, machine learning, bioengineering, data science, and software development, strengthening BioNTechs capacity to drive innovation and accelerate the development of next-generation therapeutics.
Growth of AI-Powered Drug Discovery Startups -
Startups developing AI-powered solutions are driving significant growth in the drug discovery sector. By leveraging artificial intelligence and machine learning, these companies accelerate drug development, cut costs, and improve the precision of identifying promising drug candidates. AI integration with biological data helps streamline processes like target identification, molecule screening, and clinical trial optimization. Key players include Insilico Medicine, which uses AI to design and optimize novel drug candidates, Exscientia, employing AI-driven automation for personalized medicines and higher clinical trial success, and Atomwise, which applies deep learning to efficiently predict drug interactions. These startups attract substantial investments from venture capitalists and pharmaceutical companies, fostering innovation and collaboration. Notable examples include Xaira Therapeutics, which received $1 million in funding in April 2024, and BioNTechs acquisition of AI-driven startup InstaDeep for $440 million in 2023. Through partnerships and technological advancements, AI-powered startups are transforming drug discovery, improving efficiency, reducing costs, and enhancing patient outcomes.
Biotech Firms Driving Growth Through AI-Powered Drug Discovery -
The biotechnology industry is experiencing significant growth, creating major opportunities for AI integration in drug discovery. The United States remains the global hub, hosting over 2,000 private and public companies, while Indias biotechnology sector has rapidly expanded, contributing 4.25% to GDP and projected to reach $150 billion by 2025 and $270300 billion by 2030. According to IBISWorld, 3,429 biotech businesses were operating in the U.S. as of 2023. Biotechnology drives innovation in product development, particularly in the biopharmaceutical industry, with biopharmaceuticals being the fastest-growing segment due to their efficacy and ability to treat previously untreatable diseases. In 2025, companies such as Exscientia, Atomwise, Recursion Pharmaceuticals, Chai Discovery, Antiverse, Relay Therapeutics, Xaira, LTZ Therapeutics, and Mount Sinai are leveraging AI to design molecules, predict molecular structures, and develop therapies for cancer, rare diseases, and immuno-oncology. These AI-driven innovations streamline drug development, reduce timelines, and unlock new treatment possibilities, positioning the biotechnology sector for transformative advancements globally and in India.
RECENT DEVELOPMENTS
In August 2025, XtalPi and DoveTree announced a $6 billion collaboration to co-develop new therapies. The partnership leverages AI models to predict drug-target interactions. It aims to accelerate research pipelines while reducing R&D costs. The initiative positions both companies as leaders in AI-powered pharmaceutical innovation.
In August 2025, Relay Therapeutics advanced its lead candidate RLY-2608 through clinical trials. The development uses AI to guide precision oncology drug design. Machine learning algorithms optimized molecular interactions for efficacy. This milestone demonstrates AIs impact on accelerating cancer therapy development.
In August 2025, 65LAB and Duke-NUS were awarded $1.5 million to develop an AI-driven platform for antifibrotic drug discovery. The platform targets lung and kidney diseases using machine learning algorithms. It integrates biological and chemical datasets to optimize candidate selection. The grant supports research to accelerate precision medicine development.
In July 2025, Tahoe Therapeutics secured $30 million to advance its AI-powered virtual cell models. The models simulate cellular behavior for improved drug discovery predictions. Funding will support expansion of their AI platform for preclinical research. This initiative aims to reduce reliance on traditional laboratory experiments.
In July 2025, Isomorphic Labs (Alphabet/DeepMind) announced readiness to begin human trials for its first AI-designed drugs. These drugs were developed using DeepMinds structure-predicting AI models. The approach predicts protein-ligand interactions to optimize drug efficacy. This milestone represents a major advancement in AI-driven drug discovery.
In June 2025, NVIDIA and Novo Nordisk partnered to utilize the Gefion AI supercomputer for drug discovery. The collaboration focuses on developing customized AI models for pharmaceutical research. It enables high-speed simulations of molecular interactions and protein folding. The partnership aims to accelerate the identification of promising drug candidates.
In June 2025, Merck introduced an internal generative AI platform to streamline clinical study report development. The platform is designed to speed up the delivery of medicines to patients. It uses AI to process and analyze complex clinical trial data efficiently. This innovation supports faster decision-making in drug development pipelines.
In June 2025, MIT and Recursion Pharmaceuticals launched the Boltz-2 AI model, designed to accurately predict small-molecule binding affinities. This model enhances early-stage drug discovery by improving target identification. It integrates advanced machine learning algorithms with biological datasets. The collaboration aims to accelerate preclinical research and reduce development costs.
KEY PLAYERS
Insilico Medicine, Exscientia, Atomwise, Benevolent AI, Schrodinger, Recursion Pharmaceuticals, Cyclica, Deep Genomics, Xtal Pi, Bio Symetrics, Cloud Pharmaceuticals, Numerate, Two XAR, Valo Health, Silicon Therapeutics, Bench Sci, Healx, Aria Pharmaceuticals, Peptone, and Molecular AI
Please Note: This report will be delivered by publisher within 3-4 business days of order confirmation.
Table of Contents
340 Pages
- 1 Executive Summary
- 1.1 Market Size and Forecast
- 1.2 Market Overview
- 1.3 Market Snapshot
- 1.4 Regional Snapshot
- 1.5 Strategic Recommendations
- 1.6 Analyst Notes
- 2 Market Highlights
- 2.1 Key Market Highlights by Offering
- 2.2 Key Market Highlights by Services
- 2.3 Key Market Highlights by Type
- 2.4 Key Market Highlights by Deployment
- 2.5 Key Market Highlights by Technology
- 2.6 Key Market Highlights by Application
- 2.7 Key Market Highlights by End User
- 2.8 Key Market Highlights by Therapeutic Area
- 3 Market Dynamics
- 3.1 Macroeconomic Analysis
- 3.2 Market Trends
- 3.3 Market Drivers
- 3.4 Market Opportunities
- 3.5 Market Restraints
- 3.6 CAGR Growth Analysis
- 3.7 Impact Analysis
- 3.8 Emerging Markets
- 3.9 Technology Roadmap
- 3.10 Strategic Frameworks
- 3.10.1 PORTER's 5 Forces Model
- 3.10.2 ANSOFF Matrix
- 3.10.3 4P's Model
- 3.10.4 PESTEL Analysis
- 4 Segment Analysis
- 4.1 Market Size & Forecast by Offering (2020-2035)
- 4.1.1 Software
- 4.1.2 Services
- 4.2 Market Size & Forecast by Services (2020-2035)
- 4.2.1 Maintenance and Support
- 4.2.2 Training and Consulting
- 4.2.3 System Integration
- 4.3 Market Size & Forecast by Type (2020-2035)
- 4.3.1 Small Molecule
- 4.3.2 Large Molecule
- 4.4 Market Size & Forecast by Deployment (2020-2035)
- 4.4.1 Cloud
- 4.4.2 On-Premise
- 4.5 Market Size & Forecast by Technology (2020-2035)
- 4.5.1 Machine Learning
- 4.5.2 Computer Vision
- 4.5.3 Context Awareness Computing
- 4.5.4 Natural Language Processing
- 4.6 Market Size & Forecast by Application (2020-2035)
- 4.6.1 Drug Optimization and Repurposing
- 4.6.2 Preclinical Testing
- 4.6.3 Target Identification
- 4.6.4 Others
- 4.7 Market Size & Forecast by End User (2020-2035)
- 4.7.1 Pharmaceutical and Biotechnology Companies
- 4.7.2 Contract Research Organizations (CROs)
- 4.7.3 Others
- 4.8 Market Size & Forecast by Therapeutic Area (2020-2035)
- 4.8.1 Oncology
- 4.8.2 Neurology
- 4.8.3 Cardiology
- 4.8.4 Metabolic Diseases
- 4.8.5 Others
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