Global Artificial Intelligence in Drug Screening Market Size, Trend & Opportunity Analysis Report, by Therapeutic Space (Oncology, Neurodegenerative Diseases), and Forecast, 2025–2035
Description
Market Definition and Introduction
The global artificial intelligence in drug screening market was valued at USD 1.95 billion in 2024 and is projected to surge to USD 34.07 billion by 2035, registering a remarkable CAGR of 29.7% over the forecast period (2025–2035). This exponential growth signifies a paradigm shift from the traditional pharmaceutical development process as AI-driven platforms are fast disrupting legacy R&D frameworks with unprecedented advantages in the faster and more efficient identification of novel drug candidates. As the pharmaceutical environment is going through rapid digital transformation, artificial intelligence is fast becoming the core enabler of next-gen drug discovery strategies across various therapeutic verticals, especially for oncology and neurodegenerative disorders.
AI-based drug screening schemes could transform the way scientists simulate compound interactions, model disease progression, and predict therapeutic efficacy, thanks to machine learning algorithm advances, deep neural network approaches, and quantum computing applications. These instruments allow researchers to sample through enormous data sets to identify lead candidates and de-risk early clinical trial timelines. Such a leap toward computational accuracy has become most necessary as biopharma companies turn toward complex and rare diseases, which require much more personalized therapeutic approaches.
The market is also buoyed by an increasing number of strategic collaborations between big pharma companies and AI-native biotech firms. These partnerships are accelerating the validation of AI-generated hypotheses in applying computational outputs in real-world clinical settings. Therefore, artificial intelligence emerges not only as a supportive tool for making the drug development process efficient but also as a disruptive one, impacting and shaping the entire drug development value chain, from target identification to IND submission. Countries the world over strive to cut down time-to-market and R&D overheads.
Recent Developments in the Industry
BenevolentAI expands AI-based drug discovery platform through strategic partnerships.
In April 2024, BenevolentAI established a multi-year partnership with AstraZeneca to discover new targets in chronic kidney and idiopathic pulmonary diseases. The consortium combines BenevolentAI's proprietary knowledge graph and AI inference engine to uncover previously unexplored biological mechanisms.
Insilico Medicine Advances Clinical Pipeline Via AI-Powered Small-Molecule Design
In June 2024, Insilico Medicine announced that its lead AI-generated molecule, INS018_055, progressed into Phase II testing for idiopathic pulmonary fibrosis. Such mid-stage human trials may be among the earliest for any AI-designed compound, lending credence to automated drug generation potential.
Recursion Pharmaceuticals Invests in Supercomputing Infrastructure for Phenomic Drug Screening
In January 2024, Recursion Pharmaceuticals opened its cutting-edge BioHive-2 supercomputer for powering phenotypic screening over billions of cellular images. The updated infrastructure intends to increase the speed of identification of drug candidates through an AI-based visual pattern recognition.
Market Dynamics
The Need for Precision Medicine Triggers Increased Use of AI in Drug Screening Processes
The increasing demand for personalized and precision therapeutics, especially in oncology and neurology sectors, led to the integration of AI into early drug discovery pipelines. Unlike conventional methods, AI tools can rapidly synthesize genomic, proteomic, and clinical data to pinpoint specific therapeutic targets in stratified patient populations. This capability not only enhances accuracy in hit identification but also minimizes resource wastage associated with trial-and-error drug development approaches.
Increase in Investments and Venture Funding in AI-Biotech Platforms, Firemarket Expansion
AI-oriented biotech startups continue to elicit large amounts of venture capital as investors increasingly grasp the long-term proposition regarding these intelligent screening technologies. For instance, Exscientia had recently raised $225 million in its Series D funding round in 2023, an indication of investor confidence in the scalability of AI-driven platforms in speeding up drug discovery. These investments are enabling deeper R&D, market expansion into new indications, and the adoption of more powerful computing models that enhance reliability and speed of output.
Mandating High-Throughput, AI-Powered Simulations to Complex Drug Targets and Disease Models
As more and more focus shifts on the highly heterogeneous and rare diseases, traditional wet-lab screening is too limited in terms of both scalability and cost. AI platforms allow for real-time hypothesis testing where complex pathways and polypharmacological interactions can be modeled. By using reinforcement learning and generative artificial intelligence, such systems can screen thousands of drug-like compounds in silico before validating in the lab, massively reducing timelines.
Attractive Opportunities in the Market
Emergence of AI-Biotech Collaborations – Strategic alliances accelerate AI adoption in drug R&D.
Advances in Deep Learning – Neural networks enable improved compound activity and toxicity prediction.
Automated Phenotypic Screening – Image-based AI models detect cellular response patterns in real time.
Rare Disease Therapeutics – AI enables identification of niche targets in underexplored indications.
Multi-Omics Integration – Unified analysis of transcriptomics, epigenetics, and metabolomics boosts precision.
Quantum Computing – Enhances molecular modeling accuracy for next-gen drug simulations.
Cloud-Based Screening Platforms – Democratizes AI tools across CROs and academic institutions.
AI-Driven Repurposing – Shortens drug repositioning cycles for emerging and unmet medical needs.
Report Segmentation
By Therapeutic Space: Oncology, Neurodegenerative Diseases
By Region: North America (U.S., Canada, Mexico), Europe (UK, Germany, France, Spain, Italy, Spain, Rest of Europe), Asia-Pacific (China, India, Japan, Australia, South Korea, Rest of Asia-Pacific), LAMEA (Brazil, Argentina, UAE, Saudi Arabia (KSA), Africa Rest of Latin America)
Key Market Players
BenevolentAI, Insilico Medicine, Atomwise Inc., Exscientia, BioXcel Therapeutics, Recursion Pharmaceuticals, Deep Genomics, Cyclica, Cloud Pharmaceuticals, and Aria Pharmaceuticals.
Report Aspects
Base Year: 2024
Historic Years: 2022, 2023, 2024
Forecast Period: 2025–2035
Report Pages: 293
Dominating Segments
Oncology Segment Leads AI Drug Screening Market with Customized Algorithmic Drug Design Strategies
The primary motivation for the AI's input into oncology is the complexity and heterogeneity of cancer biology. AI algorithms allow drug developers to dissect tumorigenic pathways, predict mutation-driven resistance, and design therapies with very high specificity to individual cancer genotypes. This has led to an AI-enabled drug repurposing platform that is streamlining and optimizing treatment pathways for aggressive and rare tumor types.
Neurodegenerative Diseases Gaining Ground as AI Enters the Gap in Target Identification
Neurodegenerative diseases like Alzheimer's, Parkinson's, and ALS come under the area of high unmet need, where traditional screening methods fall short, owing to limited biomarkers and slow disease progression. AI-based platforms are bridging this gap by deciphering complex neural datasets along with imaging biomarkers to yield early diagnostic pointers and therapeutic targets. These tools allow drug developers to simulate changes in neurons that are more predictive of preclinical models and better outcomes from clinical trials.
Generative AI Models Pushing Innovation for De Novo Drug Molecule Synthesis and Lead Optimization
Across both segments, generative AI tools are revolutionizing the way molecules are conceived and optimized. Using reinforcement learning and advanced molecular docking simulations, these models generate completely novel drug-like compounds tailored to the needs of specific binding sites and pharmacokinetic profiles. This drastically reduces the number of iterations needed to identify a viable clinical candidate, thereby accelerating time-to-market and reducing costs.
Key Takeaways
AI Revolution – Deep learning and neural networks enable precision-targeting in early drug discovery.
Oncology Dominance – Cancer therapeutics remain the largest and most dynamic application segment.
Neuro Innovation – AI tools unlock new potential in high-failure-rate neurodegenerative drug discovery.
Generative Molecules – Novel drug design accelerates lead identification and optimization.
Data Integration – Multi-omics platforms synthesize genomic, proteomic, and phenotypic data.
Speed to Market – Shorter development timelines attract funding and partnerships.
Smart Repurposing – AI-driven repositioning of old drugs for new diseases expands pipelines.
Computational Scalability – Supercomputing infrastructure boosts high-throughput screening capacity.
Asia-Pacific Acceleration – Regional AI investments reshape global clinical development hubs.
Cloud Transformation – AI-as-a-Service models support decentralized, real-time screening operations.
Regional Insights
North America Prefers to Drive AI in Drug Screening Market through R&D Investments and Strategic Alliances
Presently, North America's lead is the electricity of the world in drug screening for artificial intelligence, which is propelled by a biotechnology ecosystem as well as huge investments in R&D. It's not surprising that a US company comfortably finances most AI applications of drug-discovery using either private venture capital or government-funded research grants. Yet, the most important collaborations affecting the development of drug sets involve the application of AI-dedicated startups with large pharmaceutical industries.
Regulatory Frameworks Evolving around Europe for AI-Aided Drug Discovery
Europe is almost at par with countries such as the UK, Germany, and Switzerland, embracing some aspects of AI in biomedical research. Initiatives like the UK government-backed Accelerating Detection of Disease program and investments in digital health infrastructure are enabling the adoption of AI into pharmaceutical R&D. In addition, the developing EU data governance, along with interoperability standards, would enable a smoother rollout of machine learning platforms in clinical development settings.
Asia-Pacific Develops into a High-Growth Region with Strategic Focus on AI-Enhanced Biopharma Innovation
Asia Pacific is poised to record the highest CAGR during the forecast period, driven by promising AI talent pools, the production of increasing biopharmaceutical manufacturing facilities, and governments that strongly encourage digital health transformation. China, India, and South Korea are among the countries that are making serious investments in establishing AI research and development hubs and digital labs, from which will emerge a new surge of indigenous drug-discovery companies. The establishment of drug discovery companies across the Asia Pacific dramatically positions this region as a global innovation hub and strategic manufacturing partner for AI-designed therapeutics.
Latin America And The MEA Regions Migrate Gradually Toward Adoption of AI Technologies amid Growing Interests in Digital Pharma
Latin America and the Middle East & Africa are home to some of the most varied geographies on Earth, and they are just beginning to adopt AI solutions into drug discovery, although slowly through academic-industry partnerships as well as pilot projects. With improvements in digital infrastructure and cloud connectivity, these regions are expected to play an increasingly important role in distributed drug screening models as well as AI complex pharmacovigilance systems of the future.
Core Strategic Questions Answered in This Report
Q. What is the expected growth trajectory of artificial intelligence in the drug screening market from 2024 to 2035?
The artificial intelligence in drug screening market is anticipated to grow from USD 1.95 billion in 2024 to USD 34.07 billion by 2035, reflecting a robust CAGR of 29.7%. This growth is attributed to increasing use of AI in early-stage drug discovery, faster time-to-market expectations, and rising global demand for precision medicine.
Q. Which key factors are fuelling the growth of artificial intelligence in the drug screening market?
Key drivers include:
Increased demand for personalized medicine and precision therapeutics
Technological advances in deep learning, quantum computing, and generative models
Heavy investment and funding for AI-native drug discovery companies
Collaborations between pharma and AI firms are accelerating clinical translation
Growing data availability from genomics, proteomics, and clinical trials
Q. What are the primary challenges hindering the growth of artificial intelligence in the drug screening market?
Challenges include:
High computational costs and infrastructure demands
Limited validation data for AI predictions
Integration hurdles with traditional R&D workflows
Regulatory uncertainty regarding AI-generated drug candidates
Shortage of interdisciplinary talent in AI-biotech convergence
Q. Which regions currently lead the artificial intelligence in drug screening market in terms of market share?
North America dominates the market, led by the U.S., followed by Europe with a strong research base in countries such as the UK and Germany. Asia-Pacific is emerging rapidly due to investments in digital biopharma.
Q. What emerging opportunities are anticipated in the artificial intelligence in drug screening market?
Key opportunities include:
AI-based drug repurposing and rare disease targeting
Generative models for novel molecule synthesis
Cloud-enabled drug discovery platforms
AI integration in clinical trial simulation and optimization
Increasing use of predictive biomarkers in therapy development
Key Benefits for Stakeholders
The report offers a quantitative assessment of market segments, emerging trends, projections, and market dynamics for the period 2024 to 2035.
The report presents comprehensive market research, including insights into key growth drivers, challenges, and potential opportunities.
Porter's Five Forces analysis evaluates the influence of buyers and suppliers, helping stakeholders make strategic, profit-driven decisions and strengthen their supplier-buyer relationships.
A detailed examination of market segmentation helps identify existing and emerging opportunities.
Key countries within each region are analysed based on their revenue contributions to the overall market.
The positioning of market players enables effective benchmarking and provides clarity on their current standing within the industry.
The report covers regional and global market trends, major players, key segments, application areas, and strategies for market expansion.
The global artificial intelligence in drug screening market was valued at USD 1.95 billion in 2024 and is projected to surge to USD 34.07 billion by 2035, registering a remarkable CAGR of 29.7% over the forecast period (2025–2035). This exponential growth signifies a paradigm shift from the traditional pharmaceutical development process as AI-driven platforms are fast disrupting legacy R&D frameworks with unprecedented advantages in the faster and more efficient identification of novel drug candidates. As the pharmaceutical environment is going through rapid digital transformation, artificial intelligence is fast becoming the core enabler of next-gen drug discovery strategies across various therapeutic verticals, especially for oncology and neurodegenerative disorders.
AI-based drug screening schemes could transform the way scientists simulate compound interactions, model disease progression, and predict therapeutic efficacy, thanks to machine learning algorithm advances, deep neural network approaches, and quantum computing applications. These instruments allow researchers to sample through enormous data sets to identify lead candidates and de-risk early clinical trial timelines. Such a leap toward computational accuracy has become most necessary as biopharma companies turn toward complex and rare diseases, which require much more personalized therapeutic approaches.
The market is also buoyed by an increasing number of strategic collaborations between big pharma companies and AI-native biotech firms. These partnerships are accelerating the validation of AI-generated hypotheses in applying computational outputs in real-world clinical settings. Therefore, artificial intelligence emerges not only as a supportive tool for making the drug development process efficient but also as a disruptive one, impacting and shaping the entire drug development value chain, from target identification to IND submission. Countries the world over strive to cut down time-to-market and R&D overheads.
Recent Developments in the Industry
BenevolentAI expands AI-based drug discovery platform through strategic partnerships.
In April 2024, BenevolentAI established a multi-year partnership with AstraZeneca to discover new targets in chronic kidney and idiopathic pulmonary diseases. The consortium combines BenevolentAI's proprietary knowledge graph and AI inference engine to uncover previously unexplored biological mechanisms.
Insilico Medicine Advances Clinical Pipeline Via AI-Powered Small-Molecule Design
In June 2024, Insilico Medicine announced that its lead AI-generated molecule, INS018_055, progressed into Phase II testing for idiopathic pulmonary fibrosis. Such mid-stage human trials may be among the earliest for any AI-designed compound, lending credence to automated drug generation potential.
Recursion Pharmaceuticals Invests in Supercomputing Infrastructure for Phenomic Drug Screening
In January 2024, Recursion Pharmaceuticals opened its cutting-edge BioHive-2 supercomputer for powering phenotypic screening over billions of cellular images. The updated infrastructure intends to increase the speed of identification of drug candidates through an AI-based visual pattern recognition.
Market Dynamics
The Need for Precision Medicine Triggers Increased Use of AI in Drug Screening Processes
The increasing demand for personalized and precision therapeutics, especially in oncology and neurology sectors, led to the integration of AI into early drug discovery pipelines. Unlike conventional methods, AI tools can rapidly synthesize genomic, proteomic, and clinical data to pinpoint specific therapeutic targets in stratified patient populations. This capability not only enhances accuracy in hit identification but also minimizes resource wastage associated with trial-and-error drug development approaches.
Increase in Investments and Venture Funding in AI-Biotech Platforms, Firemarket Expansion
AI-oriented biotech startups continue to elicit large amounts of venture capital as investors increasingly grasp the long-term proposition regarding these intelligent screening technologies. For instance, Exscientia had recently raised $225 million in its Series D funding round in 2023, an indication of investor confidence in the scalability of AI-driven platforms in speeding up drug discovery. These investments are enabling deeper R&D, market expansion into new indications, and the adoption of more powerful computing models that enhance reliability and speed of output.
Mandating High-Throughput, AI-Powered Simulations to Complex Drug Targets and Disease Models
As more and more focus shifts on the highly heterogeneous and rare diseases, traditional wet-lab screening is too limited in terms of both scalability and cost. AI platforms allow for real-time hypothesis testing where complex pathways and polypharmacological interactions can be modeled. By using reinforcement learning and generative artificial intelligence, such systems can screen thousands of drug-like compounds in silico before validating in the lab, massively reducing timelines.
Attractive Opportunities in the Market
Emergence of AI-Biotech Collaborations – Strategic alliances accelerate AI adoption in drug R&D.
Advances in Deep Learning – Neural networks enable improved compound activity and toxicity prediction.
Automated Phenotypic Screening – Image-based AI models detect cellular response patterns in real time.
Rare Disease Therapeutics – AI enables identification of niche targets in underexplored indications.
Multi-Omics Integration – Unified analysis of transcriptomics, epigenetics, and metabolomics boosts precision.
Quantum Computing – Enhances molecular modeling accuracy for next-gen drug simulations.
Cloud-Based Screening Platforms – Democratizes AI tools across CROs and academic institutions.
AI-Driven Repurposing – Shortens drug repositioning cycles for emerging and unmet medical needs.
Report Segmentation
By Therapeutic Space: Oncology, Neurodegenerative Diseases
By Region: North America (U.S., Canada, Mexico), Europe (UK, Germany, France, Spain, Italy, Spain, Rest of Europe), Asia-Pacific (China, India, Japan, Australia, South Korea, Rest of Asia-Pacific), LAMEA (Brazil, Argentina, UAE, Saudi Arabia (KSA), Africa Rest of Latin America)
Key Market Players
BenevolentAI, Insilico Medicine, Atomwise Inc., Exscientia, BioXcel Therapeutics, Recursion Pharmaceuticals, Deep Genomics, Cyclica, Cloud Pharmaceuticals, and Aria Pharmaceuticals.
Report Aspects
Base Year: 2024
Historic Years: 2022, 2023, 2024
Forecast Period: 2025–2035
Report Pages: 293
Dominating Segments
Oncology Segment Leads AI Drug Screening Market with Customized Algorithmic Drug Design Strategies
The primary motivation for the AI's input into oncology is the complexity and heterogeneity of cancer biology. AI algorithms allow drug developers to dissect tumorigenic pathways, predict mutation-driven resistance, and design therapies with very high specificity to individual cancer genotypes. This has led to an AI-enabled drug repurposing platform that is streamlining and optimizing treatment pathways for aggressive and rare tumor types.
Neurodegenerative Diseases Gaining Ground as AI Enters the Gap in Target Identification
Neurodegenerative diseases like Alzheimer's, Parkinson's, and ALS come under the area of high unmet need, where traditional screening methods fall short, owing to limited biomarkers and slow disease progression. AI-based platforms are bridging this gap by deciphering complex neural datasets along with imaging biomarkers to yield early diagnostic pointers and therapeutic targets. These tools allow drug developers to simulate changes in neurons that are more predictive of preclinical models and better outcomes from clinical trials.
Generative AI Models Pushing Innovation for De Novo Drug Molecule Synthesis and Lead Optimization
Across both segments, generative AI tools are revolutionizing the way molecules are conceived and optimized. Using reinforcement learning and advanced molecular docking simulations, these models generate completely novel drug-like compounds tailored to the needs of specific binding sites and pharmacokinetic profiles. This drastically reduces the number of iterations needed to identify a viable clinical candidate, thereby accelerating time-to-market and reducing costs.
Key Takeaways
AI Revolution – Deep learning and neural networks enable precision-targeting in early drug discovery.
Oncology Dominance – Cancer therapeutics remain the largest and most dynamic application segment.
Neuro Innovation – AI tools unlock new potential in high-failure-rate neurodegenerative drug discovery.
Generative Molecules – Novel drug design accelerates lead identification and optimization.
Data Integration – Multi-omics platforms synthesize genomic, proteomic, and phenotypic data.
Speed to Market – Shorter development timelines attract funding and partnerships.
Smart Repurposing – AI-driven repositioning of old drugs for new diseases expands pipelines.
Computational Scalability – Supercomputing infrastructure boosts high-throughput screening capacity.
Asia-Pacific Acceleration – Regional AI investments reshape global clinical development hubs.
Cloud Transformation – AI-as-a-Service models support decentralized, real-time screening operations.
Regional Insights
North America Prefers to Drive AI in Drug Screening Market through R&D Investments and Strategic Alliances
Presently, North America's lead is the electricity of the world in drug screening for artificial intelligence, which is propelled by a biotechnology ecosystem as well as huge investments in R&D. It's not surprising that a US company comfortably finances most AI applications of drug-discovery using either private venture capital or government-funded research grants. Yet, the most important collaborations affecting the development of drug sets involve the application of AI-dedicated startups with large pharmaceutical industries.
Regulatory Frameworks Evolving around Europe for AI-Aided Drug Discovery
Europe is almost at par with countries such as the UK, Germany, and Switzerland, embracing some aspects of AI in biomedical research. Initiatives like the UK government-backed Accelerating Detection of Disease program and investments in digital health infrastructure are enabling the adoption of AI into pharmaceutical R&D. In addition, the developing EU data governance, along with interoperability standards, would enable a smoother rollout of machine learning platforms in clinical development settings.
Asia-Pacific Develops into a High-Growth Region with Strategic Focus on AI-Enhanced Biopharma Innovation
Asia Pacific is poised to record the highest CAGR during the forecast period, driven by promising AI talent pools, the production of increasing biopharmaceutical manufacturing facilities, and governments that strongly encourage digital health transformation. China, India, and South Korea are among the countries that are making serious investments in establishing AI research and development hubs and digital labs, from which will emerge a new surge of indigenous drug-discovery companies. The establishment of drug discovery companies across the Asia Pacific dramatically positions this region as a global innovation hub and strategic manufacturing partner for AI-designed therapeutics.
Latin America And The MEA Regions Migrate Gradually Toward Adoption of AI Technologies amid Growing Interests in Digital Pharma
Latin America and the Middle East & Africa are home to some of the most varied geographies on Earth, and they are just beginning to adopt AI solutions into drug discovery, although slowly through academic-industry partnerships as well as pilot projects. With improvements in digital infrastructure and cloud connectivity, these regions are expected to play an increasingly important role in distributed drug screening models as well as AI complex pharmacovigilance systems of the future.
Core Strategic Questions Answered in This Report
Q. What is the expected growth trajectory of artificial intelligence in the drug screening market from 2024 to 2035?
The artificial intelligence in drug screening market is anticipated to grow from USD 1.95 billion in 2024 to USD 34.07 billion by 2035, reflecting a robust CAGR of 29.7%. This growth is attributed to increasing use of AI in early-stage drug discovery, faster time-to-market expectations, and rising global demand for precision medicine.
Q. Which key factors are fuelling the growth of artificial intelligence in the drug screening market?
Key drivers include:
Increased demand for personalized medicine and precision therapeutics
Technological advances in deep learning, quantum computing, and generative models
Heavy investment and funding for AI-native drug discovery companies
Collaborations between pharma and AI firms are accelerating clinical translation
Growing data availability from genomics, proteomics, and clinical trials
Q. What are the primary challenges hindering the growth of artificial intelligence in the drug screening market?
Challenges include:
High computational costs and infrastructure demands
Limited validation data for AI predictions
Integration hurdles with traditional R&D workflows
Regulatory uncertainty regarding AI-generated drug candidates
Shortage of interdisciplinary talent in AI-biotech convergence
Q. Which regions currently lead the artificial intelligence in drug screening market in terms of market share?
North America dominates the market, led by the U.S., followed by Europe with a strong research base in countries such as the UK and Germany. Asia-Pacific is emerging rapidly due to investments in digital biopharma.
Q. What emerging opportunities are anticipated in the artificial intelligence in drug screening market?
Key opportunities include:
AI-based drug repurposing and rare disease targeting
Generative models for novel molecule synthesis
Cloud-enabled drug discovery platforms
AI integration in clinical trial simulation and optimization
Increasing use of predictive biomarkers in therapy development
Key Benefits for Stakeholders
The report offers a quantitative assessment of market segments, emerging trends, projections, and market dynamics for the period 2024 to 2035.
The report presents comprehensive market research, including insights into key growth drivers, challenges, and potential opportunities.
Porter's Five Forces analysis evaluates the influence of buyers and suppliers, helping stakeholders make strategic, profit-driven decisions and strengthen their supplier-buyer relationships.
A detailed examination of market segmentation helps identify existing and emerging opportunities.
Key countries within each region are analysed based on their revenue contributions to the overall market.
The positioning of market players enables effective benchmarking and provides clarity on their current standing within the industry.
The report covers regional and global market trends, major players, key segments, application areas, and strategies for market expansion.
Table of Contents
285 Pages
- Chapter 1. Market Snapshot
- 1.1. Market Definition & Report Overview
- 1.2. Market Segmentation
- 1.3. Key Takeaways
- 1.3.1. Top Investment Pockets
- 1.3.2. Top Winning Strategies
- 1.3.3. Market Indicators Analysis
- 1.3.4. Top Impacting Factors
- 1.4. Industry Ecosystem Analysis
- 1.4.1. 360’ Analysis
- Chapter 2. Executive Summary
- 2.1. CEO/CXO Standpoint
- 2.2. Strategic Insights
- 2.3. ESG Analysis
- 2.4 Market Attractiveness Analysis (top leader’s point of view on market)
- 2.5.key Findings
- Chapter 3. Research Methodology
- 3.1 Research Objective
- 3.2 Supply Side Analysis
- 3.1.1. Primary Research
- 3.1.2. Secondary Research
- 3.3 Demand Side Analysis
- 3.1.3. Primary Research
- 3.1.4. Secondary Research
- 3.2. Forecasting Models
- 3.2.1. Assumptions
- 3.2.2. Forecasts Parameters ()
- 3.3. Competitive breakdown
- 3.3.1. Market Positioning
- 3.3.2. Competitive Strength
- 3.4. Scope of the Study
- 3.4.1. Research Assumption
- 3.4.2. Inclusion & Exclusion
- 3.4.3. Limitations
- Chapter 4. Chapter 4. Industry Landscape
- 4.1. Market Dynamics
- 4.1.1. Drivers
- 4.1.2. Restraints
- 4.1.3. Opportunities
- 4.2. Porter’s 5 Forces Model
- 4.2.1. Bargaining Power of Buyer
- 4.2.2. Bargaining Power of Supplier
- 4.2.3. Threat of New Entrants
- 4.2.4. Threat of Substitutes
- 4.2.5. Competitive Rivalry
- 4.3. Value Chain Analysis
- 4.4. PESTEL Analysis
- 4.5. Pricing Analysis and Trends
- 4.6. Key growth factors and trends analysis
- 4.7. Market Share Analysis (2025)
- 4.8. Top Winning Strategies (2025)
- 4.9. Trade Data Analysis (Import Export)
- 4.10. Regulatory Guidelines
- 4.11. Historical Data Analysis
- 4.12. Analyst Recommendation & Conclusion
- Chapter 5. Global Artificial Intelligence in Drug Screening Market Size & Forecasts by Therapeutic Space 2025-2035
- 5.1. Market Overview
- 5.1.1. Market Size and Forecast By Therapeutic Space 2025-2035
- 5.2. Oncology
- 5.2.1. Market definition, current market trends, growth factors, and opportunities
- 5.2.2. Market size analysis, by region, 2025-2035
- 5.2.3. Market share analysis, by country, 2025-2035
- 5.3. Neurodegenerative Diseases
- 5.3.1. Market definition, current market trends, growth factors, and opportunities
- 5.3.2. Market size analysis, by region, 2025-2035
- 5.3.3. Market share analysis, by country, 2025-2035
- Chapter 6. Global Artificial Intelligence in Drug Screening Market Size & Forecasts by Region 2025–2035
- 6.1. Regional Overview 2025-2035
- 6.2. Top Leading and Emerging Nations
- 6.3. North America Artificial Intelligence in Drug Screening Market
- 6.3.1. U.S. Artificial Intelligence in Drug Screening Market
- 6.3.1.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.3.2. Canada Artificial Intelligence in Drug Screening Market
- 6.3.2.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.3.3. Mexico Artificial Intelligence in Drug Screening Market
- 6.3.3.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.4. Europe Artificial Intelligence in Drug Screening Market
- 6.4.1. UK Artificial Intelligence in Drug Screening Market
- 6.4.1.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.4.2. Germany Artificial Intelligence in Drug Screening Market
- 6.4.2.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.4.3. France Artificial Intelligence in Drug Screening Market
- 6.4.3.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.4.4. Spain Artificial Intelligence in Drug Screening Market
- 6.4.4.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.4.5. Italy Artificial Intelligence in Drug Screening Market
- 6.4.5.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.4.6. Rest of Europe Artificial Intelligence in Drug Screening Market
- 6.4.6.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.5. Asia Pacific Artificial Intelligence in Drug Screening Market
- 6.5.1. China Artificial Intelligence in Drug Screening Market
- 6.5.1.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.5.2. India Artificial Intelligence in Drug Screening Market
- 6.5.2.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.5.3. Japan Artificial Intelligence in Drug Screening Market
- 6.5.3.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.5.4. Australia Artificial Intelligence in Drug Screening Market
- 6.5.4.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.5.5. South Korea Artificial Intelligence in Drug Screening Market
- 6.5.5.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.5.6. Rest of APAC Artificial Intelligence in Drug Screening Market
- 6.5.6.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.6. LAMEA Artificial Intelligence in Drug Screening Market
- 6.6.1. Brazil Artificial Intelligence in Drug Screening Market
- 6.6.1.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.6.2. Argentina Artificial Intelligence in Drug Screening Market
- 6.6.2.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.6.3. UAE Artificial Intelligence in Drug Screening Market
- 6.6.3.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.6.4. Saudi Arabia (KSA Artificial Intelligence in Drug Screening Market
- 6.6.4.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.6.5. Africa Artificial Intelligence in Drug Screening Market
- 6.6.5.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- 6.6.6. Rest of LAMEA Artificial Intelligence in Drug Screening Market
- 6.6.6.1. Therapeutic Space breakdown size & forecasts, 2025-2035
- Chapter 7. Company Profiles
- 7.1. Top Market Strategies
- 7.2. Company Profiles
- 7.2.1. BenevolentAI
- 7.2.1.1. Company Overview
- 7.2.1.2. Key Executives
- 7.2.1.3. Company Snapshot
- 7.2.1.4. Financial Performance (Subject to Data Availability)
- 7.2.1.5. Product/Services Port
- 7.2.1.6. Recent Development
- 7.2.1.7. Market Strategies
- 7.2.1.8. SWOT Analysis
- 7.2.2. Insilico Medicine
- 7.2.3. Atomwise Inc.
- 7.2.4. Exscientia
- 7.2.5. BioXcel Therapeutics
- 7.2.6. Recursion Pharmaceuticals
- 7.2.7. Deep Genomics
- 7.2.8. Cyclica
- 7.2.9. Cloud Pharmaceuticals
- 7.2.10. Aria Pharmaceuticals
Pricing
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