Generative AI In Life Sciences Market Size Projects USD 1,657.02 Million by 2034

The generative AI in life sciences market size is calculated at USD 302.05 million in 2025 and is expected to reach around USD 1,657.02 million by 2034, growing at a CAGR of 20.82% for the forecasted period.

Ottawa, Sept. 30, 2025 (GLOBE NEWSWIRE) -- The global generative AI in life sciences market size was valued at USD 250 million in 2024 and is predicted to hit around USD 1,657.02 million by 2034, rising at a 20.82% CAGR, a study published by Towards Healthcare a sister firm of Precedence Research.

This market is rising because breakthroughs in AI models now enable the design of novel molecules, proteins, and trial strategies at scale, dramatically accelerating research productivity and reducing costs.

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Key Takeaways:

Generative AI in life sciences market to cross USD 250 Million in 2024.
Market projected at USD 1,657.02 Million by 2034.
CAGR of 20.82% expected in between 2025 to 2034.
North America held the major revenue share of the market by 42% in 2024.
Asia Pacific is expected to be the fastest-growing region in 2025-2034.
By technology, the novel molecule generation segment led the market in 2024.
By technology, the protein sequence design segment is expected to grow significantly in the upcoming years.
By application, the drug discovery segment dominated by capturing the largest revenue share of the market in 2024.
By application, the clinical trial segment is expected to grow at the fastest CAGR over the projected period.

Market Overview:

What Is the Market Landscape for Generative AI in Life Sciences?

In the life sciences, generative AI refers to algorithmic systems, such as generative adversarial networks, variational autoencoders, and transformer-based models, designed to create new biological entities (molecules, proteins, synthetic genes) or generate datasets in therapeutic, diagnostic, and operational settings.

Generative AI tools will continue to transform research and development in biopharma, diagnostics, and biotech, as they convert data including chemical, genomic, and clinical data, into usable designs. As a result, life sciences investors and companies are quickly implementing generative AI tools for faster and significant advancements that reduce reagent costs and explore chemical space beyond human capacity.

Market Scope:

Metric	Details
Market Size in 2025	USD 302.05 Million
Projected Market Size in 2034	USD 1,657.02 Million
CAGR (2025 - 2034)	20.82	%
Leading Region	North America share 42%
Market Segmentation	By Technology, By Application, By Region
Top Key Players	IBM Corporation, AiCure LLC, MosaicMLNVIDIA, Insilico Medicine Inc., Writer, HealthArk, Indegene, Microsoft

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Major Growth Drivers:

The increasing demand for faster drug discovery pipelines Traditional timelines for drug development are over 10 years, generative AI can design, screen, and optimize candidate molecules in silico in weeks or months, compressing the timeframes.
As genomic, proteomic, metabolomic, and clinical data grows exponentially, AI models have ample raw fuel to learn to discover patterns and propose novel designs that far exceed human expertise.
HPC, and foundational models Advances in computation infrastructure, GPU/TPU capacity, and biological foundation models (e.g., protein language models) have helped enable large generative training and inference pipelines to run in biotech settings.
Unprecedented venture capital, pharma, AI partnerships/process, and regulatory acceptance of model-based evidence is paving the way for large scale adoption and validation of use cases in drug discovery and clinical operations.

Key Drifts:

From Pilot to Platform: The Transition to Closed-Loop Discovery

The industry is transitioning from individual pilot studies to integrated, closed-loop discovery platforms that continually iterate key design-synthesis-analysis cycles to enable self-improving practical models in real-time. Generative AI is also moving beyond early discovery and into downstream workflows (regulatory submission computers, synthetic route planning, trial simulations), further complicating the relationship between AI and the complete life-science value chain.

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Significant Challenge:

Challenges, Explainability & Regulatory Trust Gaps:

Many pharmaceutical and biotech datasets remain locked in silos, gated, or non-public, which restricts generative models from generalizing to broader applications. At the same time, AI-generated proposals are also met with scepticism due to limited understanding/interpretability or biological plausibility, making it difficult for an expert (sometimes for regulators) to trust the "black box" outputs. The trust gap combined with governance changes around AI in health-care adds to the overall challenges of adopting a generative AI solution broadly.

Regional Analysis:

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The life sciences market for generative artificial intelligence (AI) is led by North America, exhibiting a drastic revenue share in recent years due to robust biotech/pharma clusters, considerable research and development (R&D) spending, a benign regulatory environment, and early adopter expertise in healthcare. Research institutions, cloud service providers, and startups are densely clustered in North America, accelerating translation from bench to bench manufacturing (B2B and B2C).

Asia Pacific is expected to demonstrate the highest compounded annual growth rate over the forecast period, fuelled by increasing human and funding for pharmaceuticals R&D, increasing healthcare funding, and generating innovative local biotech capabilities while there is a strategic adoption of generative AI to close industry capacity and capability gaps in its life science industries. Specific countries like China and India are advancing their local economies with a greater emphasis on AI-driven drug design, precision medicine, and computational biology.

Segmental Insights:

By Technology:

The novel molecule generation segment currently represents the largest share of the technology stack in generative AI for life sciences, as de novo design of small molecules as an application remains the most mature and highest revenue-generating. Many platforms are now optimizing around ligand design, ADMET filtering, and scaffold hopping.

The protein sequence design segment is forecasted to grow the fastest as model architecture and datasets evolve to support de novo enzyme design, antibody engineering, and synthetic proteins for therapeutic and diagnostic applications. As protein design models improve, there will be far richer opportunities to explore biologics beyond small molecules.

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By Application:

The application segment concerning drug discovery leads in terms of market share in the generative AI in life sciences space, as nearly every biotech and pharma activity focuses on candidate identification, hit/lead optimization, and novel therapeutic proposals, all of which are all AI-enabled.

GenAI significantly reduces the cost, time, and experimental iterations of early R&D, while the clinical trial application segment is the fastest growing. Generative AI is already being utilized to simulate outcomes of clinical trials, optimize recruitment criteria, balance cohorts, and even generate synthetic patient trajectories to support trial design and trial augmentation. These help to reduce dropout, accelerate timelines, and de-risk late staged development.

Recent Developments:

In June 2024, EvolutionaryScale raised USD 142 million to scale its biological AI platform aimed at generating novel proteins and systems; the funding supports its release of ESM3, a protein language model for biodesign.
In February 2025, Capgemini introduced a GenAI-driven protein engineering method using a specialized protein large language model (pLLM) to predict robust variants, marking a strategic push into therapeutics AI in consulting arms.
In 2025, AI model ESM3 was used to generate esmGFP, a fluorescent protein that mimics 500 million years of evolutionary divergence, demonstrating AI’s ability to surpass natural sequence space.

Browse More Insights of Towards Healthcare:

The global genomics and life science analytics sector is on a strong growth path, expected to see significant revenue expansion over the forecast period from 2025 to 2034.

The global natural language processing (NLP) market in healthcare and life sciences is gaining momentum, with a market size of USD 6.66 billion in 2024, rising to USD 8.97 billion in 2025, and projected to reach approximately USD 132.34 billion by 2034. This represents a robust CAGR of 34.74% between 2025 and 2034.

Similarly, the global life science analytics market is experiencing steady growth. From a valuation of USD 10.47 billion in 2024, it is expected to reach USD 11.27 billion in 2025 and climb to around USD 21.85 billion by 2034, expanding at a CAGR of 7.63% over the forecast period.

The global life science tools market is also set for remarkable expansion, driven by increasing R&D investments and technological advancements. The market is projected to grow from USD 156.52 billion in 2025 to USD 470.20 billion by 2034, reflecting a CAGR of 13%.

The cell and gene therapy thawing equipment market is witnessing rapid adoption. The market, valued at USD 0.96 billion in 2024, is forecast to reach USD 1.1 billion in 2025 and nearly USD 3.56 billion by 2034, growing at a CAGR of 14.24%.

Meanwhile, the cell and gene therapy manufacturing QC market segment is set to expand from USD 2.66 billion in 2024 to USD 3.11 billion in 2025, reaching approximately USD 12.35 billion by 2034, at a CAGR of 16.89%.

The cell and gene overexpression services market is projected to grow more moderately, from USD 771.40 million in 2024 to USD 805.73 million in 2025, and reaching around USD 1,192.24 million by 2034, with a CAGR of 4.45%.

Overall, the broader cell and gene solutions market is on a growth trajectory, expected to generate substantial revenue and expand into the hundreds of millions over the forecast period.

The cell and gene therapy CDMO market segment is demonstrating strong momentum, growing from USD 6.41 billion in 2024 to USD 8.2 billion in 2025, and projected to reach an impressive USD 75.32 billion by 2034, at a CAGR of 27.94%.

Similarly, the cell and gene therapy manufacturing market is anticipated to grow from USD 5.08 billion in 2024 to USD 22.88 billion by 2034, reflecting a CAGR of 16.25%.

Generative AI In Life Sciences Market Key Players List:

IBM Corporation
AiCure LLC
MosaicML
NVIDIA
Insilico Medicine Inc.
Writer
HealthArk
Indegene
Microsoft

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Segments Covered in the Report

By Technology

Novel molecule generation
Protein sequence design
Synthetic gene design
Single-cell RNA sequencing
Data augmentation for model training

By Application

Drug Discovery
Biotechnology
Medical Diagnosis
Clinical Trials
Precision and Personalized Medicine
Patient Monitoring

By Region

North America
- U.S.
- Canada
Asia Pacific
- China
- Japan
- India
- South Korea
- Thailand
Europe
- Germany
- UK
- France
- Italy
- Spain
- Sweden
- Denmark
- Norway
Latin America
- Brazil
- Mexico
- Argentina
Middle East and Africa (MEA)
- South Africa
- UAE
- Saudi Arabia
- Kuwait

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About Us

Towards Healthcare is a leading global provider of technological solutions, clinical research services, and advanced analytics, with a strong emphasis on life science research. Dedicated to advancing innovation in the life sciences sector, we build strategic partnerships that generate actionable insights and transformative breakthroughs. As a global strategy consulting firm, we empower life science leaders to gain a competitive edge, drive research excellence, and accelerate sustainable growth.

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