Artificial Intelligence in Drug Discovery Market

Artificial Intelligence in Drug Discovery Market Size

The global artificial intelligence in drug discovery market was estimated at USD 3.6 billion in 2024. The market is expected to grow from USD 4.6 billion in 2025 to USD 49.5 billion in 2034, at a CAGR of 30.1%, according to the latest report published by Global Market Insights Inc. The high use of artificial intelligence in drug discovery is growing as the increasing prevalence of complex diseases that include various chronic and life-threatening diseases, rising awareness among pharmaceutical companies, and the development of advanced form of AI-driven platforms. In addition, the growing burden of chronic and rare diseases, along with advancements in machine learning and data integration systems, further contributes to market expansion. According to Science Direct, AI-discovered molecules have an 80–90% success rate in phase I, substantially higher than previous average outcomes, stimulating market demand.

Moreover, the high adoption rate among various pharmaceutical and biotech startups along with research institutions, particularly regions with strong digital infrastructure, is stimulating innovation. For instance, NIH reports that AI has the potential to transform the drug discovery process, offering improved efficiency, accuracy, and speed, stimulating the market for AI therapeutics.

The artificial intelligence in drug discovery market is defined as the segment of the pharmaceutical and biotechnology industry focused on the application of AI technologies to accelerate and optimize the drug discovery process. This includes a range of AI-driven solutions such as deep learning models, natural language processing, predictive analytics, and generative algorithms used for target identification, lead optimization, and clinical trial design.

The market has witnessed steady growth, increasing from USD 1.8 billion in 2021 to USD 2.8 billion in 2023. The growth is driven by the rising number of AI-integrated research projects, especially among biotech firms and academic institutions in technologically advanced regions. Thus, the rapid increase in demand for faster, cost-effective drug development raises the need for robust AI platforms that rely heavily on big data analytics and cloud-based computing.

Further, growing concerns over research and development productivity, higher standards in precision medicine, and increasing shift towards personalized therapies have stimulated market growth. Additionally, the increasing adoption of advanced AI frameworks such as explainable AI, federated learning, and multimodal data integration is reshaping the future landscape of drug discovery by improving accuracy and reducing development timelines.

Moreover, national governments and pharmaceutical associations are implementing digital health and innovation programs to support AI adoption in drug discovery. These initiatives, along with regulatory approvals for AI-based platforms, are increasing the adoption of AI-driven drug discovery solutions, thereby contributing to market growth. For instance, several countries have launched AI innovation hubs and public-private partnerships to promote early-stage drug development using artificial intelligence.

The market is further strengthened by increasing investment in digital healthcare infrastructure in developing economies, particularly in the Asia-Pacific and Latin America regions. The artificial intelligence in drug discovery market is poised for significant growth owing to rising research and development initiatives aimed at developing novel AI models with improved interpretability and minimal bias. Thus, the growing demand for efficient drug discovery, coupled with advancements in AI technologies and pharmaceutical innovation, is fostering growth within the AI-driven drug discovery market.

Artificial Intelligence in Drug Discovery Market Trends

• The global AI in drug discovery market is undergoing rapid transformation, driven by the rising demand for faster, cost-effective drug development across therapeutic areas.
  
• The increasing prevalence of complex diseases such as cancer, neurological disorders, and rare genetic conditions is escalating the need for AI-powered discovery platforms.
  
• Growing interest in precision medicine and personalized therapies is expanding the baseline demand for AI tools that can analyse genomic, proteomic, and clinical data.
  
• AI is enabling early-stage intervention and target identification, reducing the time and cost associated with traditional drug discovery pipelines. The shift toward non-invasive, data-driven drug development is supported by advancements in machine learning, deep learning, and natural language processing.
  
• Pharmaceutical companies are increasingly adopting AI to optimize clinical trial design, patient recruitment, and outcome prediction, improving trial success rates.
  
• Public-private partnerships and government-backed innovation programs are accelerating AI integration in drug discovery, especially in North America and Europe.
  
• AI platforms are evolving toward cloud-based, scalable solutions, allowing broader access for biotech startups, academic institutions, and mid-sized pharma firms.
  
• Regulatory bodies are beginning to define frameworks for AI validation, ensuring transparency, reproducibility, and ethical use of AI in drug development.
  
• The market is seeing a rise in AI-powered molecule generation tools, such as generative models that design novel compounds with optimized pharmacological profiles.
  
• Integration of multi-omics data and real-world evidence is enhancing the predictive power of AI models, enabling more accurate drug candidate selection.
  
• AI is being used to repurpose existing drugs, identifying new indications and accelerating time-to-market for treatments with known safety profiles.
  
• Overall, the AI in drug discovery market is evolving toward precision-based, scalable, and collaborative ecosystems, reshaping the future of pharmaceutical innovation.
  

Artificial Intelligence in Drug Discovery Market Analysis

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The market was valued at USD 1.8 billion in 2021. The market size reached USD 2.8 billion in 2023, from USD 2.2 billion in 2022.

Based on component, the artificial intelligence in drug discovery market is categorized into software and services. The software segment accounted for 68.1% of the market in 2024 which is stimulated due to the widespread use into early stages of drug discovery, that includes processes such as compound screening, predictive analytics and others. The segment is expected to exceed USD 32.8 billion by 2034, growing at a CAGR of 29.8% during the forecast period. The software segment is further bifurcated into on-premises and cloud-based.

• The segment software dominates due to the presence of increasing demand for automation, along with precision and scalability in end users such as pharmaceutical companies. The high advancements in different technologies such as deep learning, natural language processing, are becoming the central part that accelerates the process of drug development timelines and increase success rates.
  
• The software segment has emerged as the leading category due to its ability to handle large-scale biological datasets, simulate molecular interactions, and identify novel drug candidates with high accuracy. AI software tools have significantly improved hit identification and target validation, contributing to faster and more targeted therapeutic discoveries. Additionally, the integration of AI software with electronic health records and clinical trial data enhances the personalization of drug discovery, making it a critical component in precision medicine.
  
• Moreover, the software segment is strongly supported by advancements in cloud computing, algorithmic design, and interoperability with existing lab infrastructure. Digital platforms enable better collaboration across research teams, real-time data analysis, and predictive modelling. The segment also benefits from digital health ecosystems that support patient stratification, treatment monitoring, and data-driven decision-making across pharmaceutical workflows.
  
• The services segment is projected to grow at a CAGR of 30.9%. This growth is driven by increasing demand for AI consulting, platform integration, and data annotation services. Pharmaceutical companies are increasingly outsourcing AI implementation and management to specialized service providers, also strategic partnerships between AI startups and contract research organizations (CROs), enabling tailored solutions for drug discovery and development.
  

Based on technology, the artificial intelligence in drug discovery market is segmented into machine learning, deep learning and other technology. The machine learning segment dominated the market in 2024 with a market share of 62.5% driven by the broad application in drug discovery stages. The segment is further bifurcated into supervised learning, unsupervised learning and other machine learning technologies.

• The segment has emerged as the dominant category due to its adaptability across diverse datasets and its proven success in predictive analytics. Machine learning models have significantly enhanced compound screening and biomarker identification, contributing to more efficient and targeted drug development.
  
• Its integration with real-world evidence and clinical trial data further supports personalized medicine approaches, making it a critical enabler of precision therapeutics.
  
• Moreover, the machine learning segment benefits from advancements in cloud infrastructure, scalable computing, and open-source frameworks. These tools allow for rapid model deployment, continuous learning, and real-time feedback loops. The segment also gains momentum from partnerships between pharma companies and AI startups, fostering innovation in algorithm design and data integration across drug discovery pipelines.
  
• The deep learning segment is also gaining attention, driven by its superior performance in complex pattern recognition tasks such as image-based drug screening, protein structure prediction, and literature mining. Technologies such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are increasingly used to simulate biological processes and decode molecular interactions.
  
• Additionally, the other technologies segment, which includes natural language processing, data mining and knowledge graphs, computational drug design. Though less dominant, these technologies offer innovative approaches to drug design, especially in rare disease research and combination therapy development.
  

Based on application type, the artificial intelligence in drug discovery market is segmented into molecular library screening, target identification, drug optimization and repurposing, de novo drug designing, preclinical testing and other applications. The molecular library screening segment dominated the market in 2024 with USD 1.1 billion market size, stimulated by the early-stage drug discovery and reducing the time and cost associated with traditional screening methods.

• The molecular library screening segment has emerged as the dominant category due to its ability to process millions of compounds in silico, significantly reducing the need for physical assays. These AI-driven platforms leverage deep learning and predictive modelling to identify promising candidates with optimal pharmacokinetic and pharmacodynamic profiles.
  
• Moreover, the integration of high-throughput screening data, cheminformatics, and cloud-based computing has further enhanced scalability and precision, making it a cornerstone of modern drug discovery workflows. For instance, Sigma-Aldrich has a Lopac-1280 is a compound library screening commonly involves performing high-throughput screening to identify possible targets for drug development.
  
• In contrast, the target identification segment is gaining momentum due to its transformative impact on precision medicine and disease biology understanding. AI algorithms are increasingly used to analyse multi-omics data, uncovering novel druggable targets and elucidating complex disease pathways.
  
• The preclinical testing segment remains a foundational application, particularly in validating drug safety and efficacy before clinical trials. AI tools are being used to simulate biological responses, predict toxicity, and optimize dosing strategies, thereby reducing reliance on animal models and improving translational outcomes.
  

Based on therapeutic area, the artificial intelligence in drug discovery market is segmented into oncology, neurodegenerative diseases, inflammatory, infectious diseases, metabolic diseases, rare diseases, cardiovascular diseases and other therapeutic areas. The oncology segment dominated the market in 2024 with a growing CAGR of 30.2%, the segment domination is due to the increasing cases of cancer globally and the urgent need for faster, more precise drug development.

• The oncology segment dominates the market as the cases increase and increased alignment with precision medicine and the high volume of oncology-focused research and development investments. According to WHO, more than 20 million cancer cases and about 9.7 million deaths were found in 2022.
  
• Moreover, AI-integrated platforms are enabling pharmaceutical companies to uncover hidden patterns in tumour biology, simulate drug interactions, and prioritize compounds with the highest therapeutic potential.
  
• Additionally, regulatory support for AI-based oncology trials and the rise of digital pathology and radiomics are accelerating its adoption in both early and late-stage development.
  
• The neurodegenerative diseases segment is gaining momentum due to the increasing prevalence of conditions such as Alzheimer’s, Parkinson’s, and ALS, coupled with the complexity of their pathophysiology.
  
• The inflammatory diseases segment remains a key focus area, particularly in autoimmune and chronic inflammatory conditions like rheumatoid arthritis, psoriasis, and IBD. AI is being used to model immune system responses, predict flare-ups, and identify patient subgroups most likely to benefit from targeted therapies.
  

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Based on end use, the artificial intelligence in drug discovery market is segmented into pharmaceutical and biotechnology companies, contract research organization (CROs) and other end users. The pharmaceutical and biotechnology companies segment dominated the market in 2024 with a market share of 53.2%, the segment domination is driven by growing demand for faster, cost-effective, and more precise drug development.

• The pharmaceutical and biotechnology companies’ segment is growing due to the increasing number of companies and significant investments in AI platforms, strategic collaborations with tech firms, and internal AI-driven innovation programs.
  
• Moreover, these organizations are leveraging AI to analyse real-world data, predict drug efficacy, and identify novel compounds with higher success potential. The growing emphasis on personalized medicine, coupled with regulatory support for AI-enabled drug development, has made this segment a key driver of market growth.
  
• Further, the contract research organizations (CROs) segment is changing increasingly as outsourcing of AI-driven drug discovery tasks helps reduce operational costs and accelerate timelines.
  

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North America Artificial Intelligence in Drug Discovery Market

The North America market dominated the global artificial intelligence in drug discovery industry with a market share of 47.6% in 2024. The market is stimulated due to high adoption rates among pharmaceutical companies growing among the region and increasing investment in AI infrastructure and research and development.

• The North America region has a supportive strong regulatory support and innovation ecosystems, with agencies such as FDA that help promote digital health initiatives and AI validation frameworks that escalate market adoption.
  
• Major tech and pharma collaborations in countries such as U.S. and Canada with partnerships between AI startups and leading drug manufacturers across the region are advancing the development of next-generation therapeutics and expanding the reach of AI platforms.
  

The U.S. artificial intelligence in drug discovery market was valued at USD 737.6 million and USD 930.8 million in 2021 and 2022, respectively. The market size reached USD 1.5 billion in 2024, growing from USD 1.2 billion in 2023.

• The rising incidences of chronic diseases that include diabetes, cancer and different neurodegenerative disorders is stimulating the use of AI tools that provide faster and more targeted drug development.
  
• Precision medicine initiatives in the U.S. are driving demand for AI platforms that can analyse genomic and clinical data to create personalized therapies.
  
• The expansion of telehealth services is enabling remote patient monitoring and decentralized clinical trials, supporting AI-driven drug discovery, drug screening and improving access to diverse datasets.
  

Europe Artificial Intelligence in Drug Discovery Market

Europe market accounted for USD 876.7 million in 2024 and is anticipated to show lucrative growth over the forecast period.

• Rising awareness and adoption of AI-powered drug discovery services in Europe, along with increasing government initiatives to strengthen digital healthcare infrastructure, is anticipated to drive growth across the region.
  
• Advancements in AI-driven drug discovery technologies, including predictive modelling, generative molecule design, and multi-omics integration, are boosting demand for scalable and cost-effective solutions throughout Europe.
  
• Countries such as UK, Italy, and Spain are strengthening national production of drugs, improving access to high-cost advancements for patients.
  

Germany artificial intelligence in drug discovery market is anticipated to witness considerable growth over the analysis period.

• Germany is emerging as a strategic center for AI-driven drug discovery, with companies such as PharmAI and Molecular Health leading innovation in predictive modeling, clinical decision support, and data-driven therapeutic development.
  
• The present developing companies have been investing and taking initiatives for the development of drug using AI. For instance, in September 2025 the partnership announced by Merck KGaA and Siemens to accelerate AI deployment across drug discovery and biomanufacturing, integrating digital workflows, automation, and scientific intelligence platforms to move therapies faster from lab to patient.
  
• Germany’s strong focus on artificial intelligence in precision medicine and expanding telehealth infrastructure is enabling scalable, personalized drug discovery services, supported by robust academic research and government-backed digital health initiatives.
  

Asia Pacific Artificial Intelligence in Drug Discovery Market

The Asia Pacific market is anticipated to grow at the highest CAGR of 30.9% during the analysis timeframe.

• The region consists of key markets including China, India, Japan, South Korea, and Australia, where rising diseases and growing healthcare investments are stimulating demand for advanced drug solutions.
  
• Additionally, healthcare system and high disposable income levels enhance access to professional drug solutions.
  

China artificial intelligence in drug discovery market is predicted to grow significantly over the forecast period.

• The high number of patents in the country related to the artificial intelligence in drug discovery industry is stimulating the market, as China leads globally in AI-driven drug discovery patent filings, with a significant portion focused on generative AI applications in pharmaceuticals; according to DrugPatentWatch, this positions China as a major force in pharmaceutical innovation.
  
• Moreover, multibillion-dollar partnerships with global pharma giants highlight China's growing influence, escalated by fast development timelines, lower costs, and a state-backed biotech ecosystem.
  
• In Q1 2025, Chinese companies accounted for 32% of global biotech licensing deal value, up from 21% in 2023 and 2024, according to Jefferies, reflecting China’s rapid shift from generic manufacturing to cutting-edge drug discovery leadership.
  

Latin America Artificial Intelligence in Drug Discovery Market

Brazil is experiencing significant growth in the Latin America market due to the increasing demand for advanced healthcare technologies and precision medicine solutions.

• The rising prevalence of complex diseases in Brazil, particularly among aging populations and individuals with lifestyle-related risk factors, is driving demand for AI-powered drug discovery platforms. Hospitals, research institutions, and pharmaceutical companies are facing increased pressure to accelerate drug development timelines, prompting the adoption of AI-driven strategies to improve efficiency and outcomes.
  
• Government efforts to digitize healthcare, promote innovation in pharmaceutical research, and strengthen regulatory frameworks are stimulating the adoption of artificial intelligence in drug discovery across public and private health sectors.
  

Middle East and Africa Artificial Intelligence in Drug Discovery Market

Saudi Arabia market is poised to witness substantial growth in Middle East and Africa artificial intelligence in drug discovery industry during the forecast period.

• The increasing complexities in disease profiles and improving diagnostic capabilities are stimulating market demand for AI-driven personalized drug discovery approaches in Saudi Arabia, which is driving market growth.
  
• Government incentives under Vision 2030, along with the expansion of digital health infrastructure and local pharmaceutical innovation, are escalating demand across public and private healthcare sectors. These efforts are modernizing healthcare delivery and accelerating drug development nationwide.
  

Artificial Intelligence in Drug Discovery Market Share

Leading companies such as NVIDIA, Insilico Medicine, Exscientia, BenevolentAI, and Google (DeepMind) collectively hold approximately 45% of the global artificial intelligence in drug discovery market share. These firms maintain dominance through cutting-edge AI platforms, strategic pharmaceutical collaborations, proprietary algorithms, and continuous innovation in drug development pipelines.

NVIDIA holds a strong competitive advantage with its GPU-accelerated computing infrastructure, enabling high-throughput simulations and deep learning models that power drug discovery workflows across biotech and pharma sectors.

Insilico Medicine is recognized for its end-to-end AI drug discovery platform, integrating generative models and biological data to identify novel targets and optimize lead compounds. Its rapid progression from target identification to clinical trials has positioned it as a leader in AI-driven therapeutics.

Exscientia supports global pharmaceutical innovation with its AI-designed molecules and precision medicine approach, leveraging patient data to tailor drug candidates. Its collaborations with major pharma companies and focus on adaptive learning systems enhance its market reach.

BenevolentAI leads in knowledge graph-based drug discovery, combining biomedical data with machine learning to uncover hidden relationships and accelerate hypothesis generation. Its emphasis on rare and complex diseases strengthens its competitive edge.

Google (DeepMind) contributes to the market with advanced neural network architectures and protein folding breakthroughs, such as AlphaFold, which have revolutionized structural biology and target validation in drug discovery.

New entrants and niche players such as Atomwise, Deep Genomics, Cyclica, Deargen, LinkGevity, Examol, Helical, DenovAI Biotech, Orakl Oncology, AVAYL, Aevai Health, 9Bio Therapeutics, Aureka Biotechnologies, and chAIron are disrupting the market with innovative platforms and specialized algorithms. Their emphasis on mutation-specific drug design, regional trial networks, and AI-native pipelines positions them as agile competitors in the evolving landscape of precision drug discovery.

Meanwhile, companies such as IBM Corporation, F. Hoffmann-La Roche, and Sanofi contribute to broader AI drug discovery categories, supporting expanded therapeutic options, data integration initiatives, and global access strategies.

Artificial Intelligence in Drug Discovery Market Companies

Few prominent players operating in the artificial intelligence in drug discovery industry includes:

• 9Bio Therapeutics
• Aevai Health
• Atomwise
• Aureka Biotechnologies
• AVAYL
• BenevolentAI
• chAIron
• Cyclica
• Deargen
• Deep Genomics
• DenovAI Biotech
• Examol
• Exscientia
• Google (DeepMind)
• Helical
• IBM Corporation
• Insilico Medicine
• LinkGevity
• Microsoft
• NVIDIA Corporation
• Orakl Oncology
  
• NVIDIA

NVIDIA leads the artificial intelligence in drug discovery market with approximately 14.2% market share as its high-performance computing platforms and GPU-accelerated AI frameworks, which are widely adopted across pharmaceutical research and development pipelines. Its strength lies in enabling large-scale simulations, deep learning model training, and integration with bioinformatics tools. NVIDIA’s commitment to AI infrastructure, cloud-based drug discovery solutions, and strategic partnerships with biotech firms makes it a foundational technology provider for innovation in life sciences.

• Insilico Medicine

Insilico Medicine holds a strong position with its end-to-end AI drug discovery platform, combining generative chemistry, target identification, and clinical trial design. Its dual focus on biology and chemistry enables rapid development of novel therapeutics. Insilico’s strategic collaborations, emphasis on aging-related diseases, and leadership in AI-native drug development pipelines enhance its appeal among pharmaceutical companies and research institutions globally.

• Exscientia

Exscientia drives market growth with its AI-designed drug candidates and precision medicine approach, offering faster and more targeted therapeutic development. Its platform integrates patient data, molecular design, and adaptive learning systems to optimize drug efficacy and safety. Supported by global clinical trials and a strong focus on personalized treatment models, Exscientia is positioned as a leader in transforming traditional drug discovery into a data-driven, patient-centric process.

Artificial Intelligence in Drug Discovery Industry News

• In September 2025, Parexel partnered with Weave Bio to accelerate regulatory submission processes using Weave’s AI-native automation platform. This initiative reinforced the role of AI in expediting late-stage drug development and regulatory readiness within the AI-driven drug discovery ecosystem.
  
• In January 2024, Molecule AI advanced its position in AI-driven drug discovery through collaborations with IIT Delhi and a clinical research team in New Zealand. These efforts reinforced Molecule AI’s role in accelerating early-stage drug discovery using AI-based design and predictive modeling.
  
• In July 2023, BioNTech completed its acquisition of InstaDeep, a global AI and machine learning company. This move strengthened BioNTech’s position in the AI-driven pharmaceutical innovation landscape.
  

The artificial intelligence in drug discovery market research report includes in-depth coverage of the industry with estimates and forecast in terms of revenue in USD Million from 2021 - 2034 for the following segments:

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Market, By Component

• Software
  • On-premises
  • Cloud-based
• Services

Market, By Technology

• Machine learning                
• Deep learning
  • Supervised learning 
  • Unsupervised learning        
  • Other machine learning technologies        
• Other technologies

Market, By Application Type

• Molecular library screening
• Target Identification
• Drug optimization and repurposing
• De novo drug designing
• Preclinical testing
• Other applications

Market, By Therapeutic Area

• Oncology
• Neurodegenerative diseases
• Inflammatory
• Infectious diseases
• Metabolic diseases
• Rare diseases
• Cardiovascular diseases
• Other therapeutic areas

Market, By End Use

• Pharmaceutical and biotechnology companies
• Contract research organization (CROs)
• Other end users       

The above information is provided for the following regions and countries:

• North America
  • U.S.
  • Canada
• Europe
  • Germany
  • UK
  • France
  • Italy
  • Spain
  • Netherlands
• Asia Pacific
  • China
  • Japan
  • India
  • Australia
  • South Korea
• Latin America
  • Brazil
  • Mexico
  • Argentina
• Middle East and Africa
  • South Africa
  • Saudi Arabia
  • UAE