Revenue, 2025

USD 2.15 Billion

Forecast, 2035

USD 12.82 Billion

CAGR, 2026-2035

19.55%

Report Coverage

United States

U.S. AI in Biotechnology Market Trends for 2026

The U.S. AI in biotechnology market size was estimated at USD 2.15 billion in 2025 and is predicted to increase from USD 2.57 billion in 2026 to approximately USD 12.82 billion by 2035, expanding at a CAGR of 19.55% from 2026 to 2035.

Gradually rising chronic issues, cancer cases are propelling the demand for advanced and novel therapeutics by using AI-powered solutions in drug discovery & development. Ongoing R&D pipelines are increasingly leveraging AI algorithms to reduce the comprehensive costs and time consumption.

Key Takeaways

• United States AI in biotechnology market to crossed USD 2.57 billion by 2026.
• Market projected at USD 12.82 billion by 2035.
• CAGR of 19.55% expected in between 2026 to 2035.
• The AI in biotechnology market is expected to grow from $3.89 billion in 2025 to $4.63 billion in 2026 and reach $22.23 billion by 2035, at a CAGR of 19.04%
• By primary application/use case, the drug discovery & lead generation segment led the market by 25% in 2025.
• By primary application/use case, the agriculture/industrial biotech applications segment is expected to grow fastest in the coming years.
• By core AI technology, the classical ML/deep learning models segment dominated the U.S. AI in biotechnology market by 40% in 2025.
• By core AI technology, the generative AI segment is expected to grow at a rapid CAGR during 2026-2035.
• By commercial model, the SaaS/cloud AI platforms segment was dominant in the market by 40% in 2025 & is expected to witness the fastest growth in the studied years.
• By end user/buyer type, the large pharma & big biotech segment registered dominance in the market by 40% in 2025.
• By end user/buyer type, the biotech startups & virtual biotechs segment is expected to grow rapidly during the forecast period.

What are the Prominent Drivers in the U.S. AI In Biotechnology?

This mainly leverages machine learning, deep learning, & sophisticated algorithms in the study of complex biological data, surge drug discovery, protein engineering, & precision medicine. The overall U.S. AI in biotechnology market is fueled by a rise in the need for expedited drug discovery & development, where AI assists in discovering drug targets, estimating molecular interactions, etc. The growing demand for tailored medicine is driving the adoption of AI in the analysis of huge datasets. Moreover, the key pharmaceutical players in the U.S. are joining with AI-specialised firms to implement cutting-edge algorithms & improve their research capabilities.

Trends & Future Outlook of the U.S. AI In Biotechnology Market

Focused on Protein Binder Design

Companies are employing AI tools, such as RFdiffusion and AlphaFold, to generate protein binders with sub-Ångström structural fidelity, which allows the targeting of previously undruggable proteins.

Shifting Towards Digital Cell & Simulation

Several key firms are establishing digital twins of cells and molecules to simulate behavior, which lowers wet-lab experimentation, and prominently uses AI to enhance biomanufacturing processes & cell programming.

Transforming Explainable AI (XAI)

A notable effort has been taken in revolutionizing ‘interpretable’ AI models to offer understandable reasons for their decisions, which is essential for regulatory approval in clinical diagnostics.

Latest Investments & Alliances in the U.S. AI In Biotechnology Market

• In October 2025, Algen Biotechnologies partnered with AstraZeneca to foster AI-driven drug discovery in immunology.
• In September 2025, GSK plc invested $30 billion across the United States in advanced manufacturing facilities and AI and advanced digital technologies.
• In August 2025, Debut secured $20M in investment to speed up the progression of its proprietary, AI-based ingredient discovery platform to expand skin longevity innovation & scale its formulation business in the US & Asia.

Executive Summary Table

Segmentation Analysis

Primary Application/Use Case Insights

How did the Drug Discovery & Lead Generation Segment Dominate the Market in 2025?

Explanation

• The Drug Discovery & Lead Generation segment dominated the market in 2025 with a 25% share, driven by its critical role in identifying potential drug candidates and initiating the development process.
• Preclinical Research & Biomarker Discovery accounted for 15%, focusing on early-stage research to identify biomarkers and evaluate drug efficacy, though it did not dominate as much as drug discovery.
• Clinical Development & Trial Optimization held 20%, growing in importance as companies aim to streamline clinical trials and improve drug approval efficiency.
• Bioprocessing & Manufacturing Optimization (PAT, Yield, QC) represented 10%, focusing on improving manufacturing processes, but with a smaller share compared to discovery and clinical stages.
• Diagnostics & Companion-Diagnostics (AI for Image/Omics Readouts) also accounted for 10%, gaining momentum with the integration of AI in diagnostics, but still a smaller segment compared to drug discovery.
• Agriculture/Industrial Biotech Applications held 20%, driven by the growing demand for biotechnology solutions in agriculture and industrial sectors, although not as dominant as drug discovery and clinical development.

In 2025, the drug discovery & lead generation segment held a major share of the U.S. AI in biotechnology market by 25%. Rising demand for new treatments in the accelerating cancer, infectious diseases, and chronic issues, drives the need for novel drug molecules & its discovery efforts. Whereas, for this, generative AI, deep learning, and advanced molecular docking are enabling the development of new, stable proteins and small molecules with enhanced pharmacokinetics. Also, certain firms are using knowledge graphs to find pathogenic genes & select drug targets.

Agriculture/Industrial Biotech Applications

In the future, the agriculture/industrial biotech applications segment is estimated to show rapid growth. Ongoing emphasis on increasing crop resilience, i.e., CRISPR, synthetic biology, sustainable livestock management, & improving bioprocessing is propelling the segmental expansion. Trends mainly include the utilization of AI and remote sensing to track soil carbon sequestration and ensure sustainability metrics. Also, the wider population is leveraging AI-driven computer vision on tractors to monitor fruit size, color, and load. Specific virtual screening supports designing new molecules to target specific proteins in weeds, pests, & diseases.

Core AI Technology Insights

Which Core AI Technology Led the U.S. AI in Biotechnology Market in 2025?

Explanation

• Classical ML/Deep Learning models led the market in 2025 with a 40% share, driven by their broad applicability and proven effectiveness in solving complex problems across various industries.
• Generative AI accounted for 25%, gaining momentum for its ability to create new content and solutions, particularly in drug discovery and design.
• Graph Neural Networks (molecular GNNs) represented 10%, growing in popularity for their ability to model molecular structures and predict interactions, though still a niche compared to other AI models.
• Digital Twins & Physics-Hybrid Models captured 10%, gaining traction in industries like manufacturing and healthcare for simulating and optimizing real-world systems, but not as dominant as ML models.
• Explainable AI/Uncertainty Quantification also held 10%, focusing on improving transparency and trust in AI models, especially in critical applications like healthcare and finance.
• NLP/Knowledge Graphs for Literature & IP Mining represented 5%, growing in importance for extracting valuable insights from vast amounts of literature and intellectual property, but still a smaller segment compared to other AI applications.

The classical ML/deep learning models segment captured the dominating share of the market by 40% in 2025. Firstly, classical ML offers strong, interpretable, and computationally potent solutions for smaller, structured datasets, whereas deep learning manages unstructured data & high-dimensional, vast datasets. Firms are shifting towards transformers to analyze biological sequences as language, and which anticipate structures of all molecules & their interactions. However, models including ESM-2 enable direct 3D structure prediction from a single sequence, with the elimination of the time-consuming multiple sequence alignments (MSA).

Generative AI

Moreover, the generative AI segment is anticipated to expand fastest. Gen AI has numerous advantages, like minimal R&D time by 23–38% and expenditures by 8–15%, with uses in emerging novel antibodies, enzymes, and targeted gene therapies. Also, it is revolutionizing the biotech sector through molecular design, predictive analytics, and automated workflows. The U.S. biotechnology industry is focusing on broader use of gen AI to integrate AI into wet lab experimentation, fostering AI-developed drugs into clinical trials, and establishing ‘digital cell’ models.

Commercial Model Insights

Which Commercial Model Dominated the U.S. AI in Biotechnology Market in 2025?

Explanation

• SaaS/Cloud AI platforms led the market in 2025 with a 40% share, driven by the growing demand for flexible, scalable AI solutions that are easy to integrate and maintain.
• Collaborative partnerships/discovery alliances accounted for 20%, gaining momentum as companies increasingly collaborate to leverage AI and data for drug discovery and innovation.
• Fee-for-service (project CRO style engagements) held 10%, offering specialized services but with a smaller share compared to more scalable deployment models like SaaS.
• Licensed on-prem deployments (large pharma) represented 15%, favored by large pharmaceutical companies for their control over data and infrastructure, but not as dominant as cloud solutions.
• Model & dataset licensing/marketplaces captured 15%, growing in importance as companies look to share and monetize data and AI models, though it remains smaller than SaaS-based platforms.

In 2025, the SaaS/cloud AI platforms segment led the market by 40% in 2025 & is predicted to expand rapidly. Its dominance is driven by its high-speed, collaborative, & scalable digital solutions in R&D processes.  Also, these platforms allow biotech firms to employ advanced AI models without the requirement for huge, upfront in-house infrastructure investments. Across the U.S., leaders like Culture Biosciences are developing ‘Amazon Web Services for biotechnology,’ which enables researchers to handle bioreactors & continue experiments through the cloud.

End User/Buyer Type Insights

Why did the Large Pharma & Big Biotech Segment Lead the Market in 2025?

Explanation

• Large Pharma & Big Biotech led the market in 2025 with a 40% share, driven by their significant resources and widespread adoption of AI for drug development and manufacturing processes.
• Biotech Startups & Virtual Biotechs accounted for 25%, gaining momentum due to their agile, innovative approaches and increasing reliance on AI to accelerate research and product development.
• CROs/CDMOs using AI internally represented 15%, leveraging AI to optimize clinical trials and manufacturing processes, but did not dominate compared to larger pharmaceutical companies.
• Academic & Translational Research Centers held 10%, focusing on fundamental research and AI-driven discoveries, but with a smaller share due to their limited resources compared to commercial sectors.
• Diagnostics/Agri-bio companies also captured 10%, growing with the increasing use of AI for disease diagnostics and agricultural biotechnology applications, though still a smaller segment compared to pharma and biotech.

The large pharma & big biotech segment held the biggest share of the U.S. AI in biotechnology market by 40% in 2025. Specifically, Pfizer, AstraZeneca, Roche, and Novartis, like giant companies, are massively investing in AI to simplify pipelines. Many large pharma leaders are joining with AI specialists, such as Sanofi with Insilico and Exscientia, to achieve expertise in computational design, target detection, & trial improvement.

Biotech Startups & Virtual Biotechs

On the other hand, the biotech startups & virtual biotechs segment is estimated to register rapid expansion. Numerous of these types of firms are moving from pure drug discovery to AI-native platforms that integrate data generation, molecular design, and clinical development. However, virtual biotechs are widely using AI to control 100% of their preclinical and clinical requirements via alliances and automated labs, which lowers capital investment in physical infrastructure & maintain higher R&D speed.

AI in Biotechnology Market Growth

The AI in biotechnology market size reached US$ 3.89 billion in 2025 and is anticipate to increase to US$ 4.63 billion in 2026. By 2035, the market is forecasted to achieve a value of around US$ 22.23 billion, growing at a CAGR of 19.04%.

Regional Insights

Coastal Hubs Lead AI Innovation

The U.S. AI in biotechnology market is growing rapidly, dominated by intense activity in California and Massachusetts, which act as primary hubs for AI-driven drug discovery and genomics. San Francisco and Boston concentrate top-tier talent, venture capital funding, and major AI-biotech partnerships, driving the adoption of machine learning in molecular modeling. These regions focus on accelerating early-stage R&D, utilizing advanced AI platforms to reduce the time from target identification to clinical trials.

Texas and Mid-Atlantic Boost Adoption

Growth is expanding significantly in Texas and the Mid-Atlantic, driven by robust institutional investments and the presence of advanced healthcare systems, particularly in medical centers like Houston and Philadelphia. These areas are increasingly implementing AI to optimize biopharmaceutical manufacturing and supply chain logistics, reflecting a shift from pure research toward operational efficiency. The integration of AI tools for personalized medicine is also a major focus, driven by regional partnerships and growing data availability.

Midwest and Southeast Accelerate Partnerships

The Midwest and Southeast regions are experiencing growth through increased collaborations between AI tech companies and traditional pharmaceutical manufacturers. States like North Carolina are leveraging their research triangle to foster AI in agricultural biotechnology and agricultural-based precision medicine. This regional growth is supported by a surge in demand for AI-driven analytics to improve diagnostic accuracy, bridging the gap between clinical research and commercialized biotech solutions.

Digital Transformation Drives National Growth

Nationwide, the adoption of cloud-based AI solutions is empowering regional hubs to share data, enabling the rapid development of novel biopharmaceuticals. While California and Massachusetts remain key, digital infrastructure allows for widespread AI adoption in drug design and personalized patient care across the country. Key players, including NVIDIA and regional biotech companies, are standardizing the use of AI to analyze vast biological datasets, marking a comprehensive shift towards intelligent biotechnology workflows.

Key Players' Offerings in the U.S. AI in Biotechnology Market

SWOT Analysis

Strengths

• Particularly, AI platforms increasingly lower the time required to find drug candidates and boost the drug discovery pipeline.
• Advanced AI tools improve patient treatment strategies by assessing unique biological data and estimating responses to therapies.

Weaknesses

• Restricted transparency results in hesitancy in the medical and regulatory communities to believe AI-assisted decisions for critical purposes.
• The US FDA is investing in frameworks, including "Good Machine Learning Practice" (GMLP), but recent approval pathways were not created for adaptive algorithms.

Opportunities

• AI will be implemented to develop digital twins based on multi-omics, physiological, and clinical data to simulate drug effects, predict adverse events, & refine dosages before treatment.
• Also, AI will allow the design of synthetic biological pathways, altering bacteria or cells for elevated production of therapeutic proteins and enzymes.

Threats

• Sometimes, firms may face unsecured APIs and leaked credentials, which shows a substantial risk to securing sensitive patient information (PHI/PII).
• Dependent on improper or biased data can result in high-profile failures and reputational damage.

Recent Developments in the U.S. AI in Biotechnology Market

• In March 2026, Dyno Therapeutics, Inc. unveiled Dyno Psi-Phi, a suite of AI-driven protein design tools to support therapeutic developers in designing sequence-based medicines.
• In January 2026, insitro, the AI therapeutics company, acquired CombinAbleAI and rolled out insitro’s TherML (Therapeutic Machine Learning) platform. 

Segments Covered in the Report

By Primary Application/Use Case

• Drug discovery & lead generation
• Preclinical research & biomarker discovery
• Clinical development & trial optimization
• Bioprocessing & manufacturing optimization (PAT, yield, QC)
• Diagnostics & companion-diagnostics (AI for image/omics readouts)
• Agriculture/industrial biotech applications

By Core AI Technology

• Classical ML/Deep Learning models
• Graph Neural Networks (molecular GNNs)
• Generative AI
• NLP/Knowledge graphs for literature & IP mining
• Digital twins & physics-hybrid models
• Explainable AI/uncertainty quantification

By Commercial Model

• SaaS/Cloud AI platforms
• (platform subscriptions, API access)
• Collaborative partnerships/discovery alliances (shared milestones), Fee-for-service (project CRO style engagements)
• Licensed on-prem deployments (large pharma)
• Model & dataset licensing/marketplaces

By End User/Buyer Type

• Large Pharma & Big Biotech
• Biotech Startups & Virtual Biotechs
• CROs/CDMOs using AI internally
• Academic & translational research centers
• Diagnostics/Agri-bio company