Generative AI in the biotech market is the process of using artificial intelligence algorithms to design and generate novel solutions that are used in drug discovery, protein engineering, genetic research, and other biotech-related applications. AI models are trained on vast amounts of biological data, scientific literature, and experimental results to learn patterns and correlations within the data which is used to generate new ideas, hypotheses, or designs that can be further explored and validated by researchers in the biotech industry. It can fasten the discovery and development process of different useful drugs, revealing their hidden important function into complex biological systems, and is known to offer customized treatments and therapies aimed at individual patients.
The adoption of generative AI in the biotech market is driven by increasing demand for advanced technologies, along with the rise in global population and the requirement for personalized medication. The requirement to increase customized medicines surges the integration of generative AI into biotech research and development processes. Different types of AI tools are used to allow researchers to explore new frontiers, optimize processes, and unlock novel insights that drive progress and transformation in biotech.
Key Takeaways
- Market size: The global generative AI in biotech market size is expected to grow by 602.8 million, at a CAGR of 25.8 % during the forecasted period of 2025 to 2033.
- Market Definition: Generative AI in biotech uses the application of AI techniques to generate genetic profiles and models, solve complex problems, and drive innovation within the biotech industry.
- Technology Analysis: Natural computing processing is projected to be the dominant force in the market, capturing the largest revenue share of 41.3% in 2024.
- Application Analysis: Drug discovery is expected to witness significant growth with the highest revenue share of 41.5% throughout the forecast period.
- End User Analysis: Pharmaceutical companies are forecasted to hold the largest market share of 39.6 % and dominate the generative AI in the biotech market in 2024.
- Regional Analysis: North America is anticipated to dominate the generative AI in the biotech market, capturing a revenue share of 39.1 % in 2024.
Use Cases
- Use of Natural Language Processing: NLP, is used to extract insights from vast amounts of biomedical literature, patents, and clinical notes which helps researchers to stay updated with the latest advancements, identify trends, and generate hypotheses for further investigation.
- Clinical Trial Optimization: Generative AI helps in optimizing clinical trial design by identifying suitable patient cohorts, predicting trial outcomes, and optimizing treatment protocols. This improves trial efficiency, reduces costs, and accelerates the development of new treatments.
- Disease Diagnosis and Prediction: Generative AI algorithms analyze medical imaging data, genetic markers, and clinical data to aid in disease diagnosis, prognosis, and risk prediction which contributes to early detection, precise diagnosis, and better management of diseases.
- Drug Repurposing: Generative AI can identify existing drugs that may have the potential for repurposing in treating new indications as this approach saves time and resources in drug development and expands the therapeutic options available.
Market Dynamic
Market Driver
Enhanced drug delivery system:Generative AI in the biotech market is experiencing transformation due to the use of the AI algorithm which smoothens drug discovery and development processes in the biotechnology sector. It allows pharmaceutical companies to design, optimize, and forecast the efficiency and safety of drug molecules and compounds faster and more accurately which drives the growth of this market. AI platforms and companies are capable of identifying usable drugs much earlier in the development cycle which potentially saves time and reduces the cost of drugs.
Demand for Personalized medicine:Medical treatments are becoming more patient-centric which extensively use personalized medicine further driving the growth of biotech. AI is useful in identifying the characteristics of patients and provides customized therapies according to their disease which fuels the growth of biotech market. This technique improves patient outcomes by ensuring treatments align precisely with individual genetic profiles, medical histories, and response patterns.
Market Restraints
High cost:AI-powered platforms are expensive which poses a challenge to the growth of the biotech market using generative AI as developing, testing, and implementing, these technologies require a heavy financial investment for this. A high financial threshold creates a problem for small biotech organizations as it limits their ability to enter effectively in the market.
Regulatory Hurdles:AI-powered platforms follow strict regulatory standards to ensure safety, efficacy, and precision which are imposed by the governments. It is highly impossible to achieve these standards as it involves a complex and time-consuming process of validation, verification, and approval by regulatory authorities which can delay its introduction into the market, and hinders the growth of the market.
Opportunities
Drug Discovery:Biotech firms are continuously exploring new chemical spaces that accelerate the identification of promising drug candidates, and streamline the entire discovery process through advanced algorithms which is capable of generating and optimizing new molecules. It also reduces the dependency on labor-intensive screening methods which reduces the time and cost of the drug delivery system.
Expansion of Protein Engineering.
Companies are designing proteins with customized properties with the help of artificially made components by using
generative AI models trained on extensive datasets of biological information. Innovation powered by AI allows the creation of enzymes for industries, therapeutic proteins, and other biologically active compounds, opening doors for product development in
pharmaceuticals, agriculture, and biomanufacturing.
Market Trends
Rising Momentum in AI-Biotech Partnerships:
There is an increase in collaborative efforts between AI and biotech firms which uses AI's robust data-processing capabilities alongside the biotech sector's profound knowledge of biological mechanisms, culminating in the creation of groundbreaking AI-driven platforms for drug discovery and development.
Research Scope and Analysis
By Technology
Natural computing processing (NLP) is likely to dominate generative AI in the biotech market with the largest revenue
share of 41.3% by the end of 2024. This dominance is due to NLP’s vital role in analyzing intricate biological data which is often unorganized and extensive. These tools offer to obtain valuable insights from scientific literature, genetic data, and clinical reports, which accelerate drug discovery processes and refine predictive models for patient outcomes.
It helps identify pattern recognition and extract a vast amount of data from biomedical text datasets. It is widely applicable in analyzing patient data to identify novel drugs by using modern AI techniques like generative adversarial networks and variational auto encoders which are used in generating synthetic biological data and understanding genetic variations and their implications in diseases.
Reinforcement learning is capable of optimizing therapeutic strategies, enhancing drug development pipelines, and facilitating adaptive learning processes which positions it as a valuable AI technology in driving efficiency and innovation.
By Application
Drug discovery is expected to be the dominant this segment based on application and capturing a market share of 41.5% in 2024 due to the adoption of generative AI in the formation of new drug compounds, optimizing main ingredients, and forecasting the effectiveness of the drug and its potential side effects on humans. This technology smoothen the time-consuming and resource-intensive drug development system which positioned this as the main technology for increasing the innovation and efficiency of biotech operation.
The use of AI algorithms reduces the dependency on labor screening methods and decreases the cost involved in the creation of new drugs which drives the growth of this segment. Further, protein engineering is significantly growing in the biotech market as companies are designing proteins with precise functions, and enhancing natural properties which enables the creation of enzymes for industries, therapeutic proteins, and other biologically active compounds, opening doors for product development in pharmaceuticals, agriculture, and biomanufacturing.
Genomics and bioinformatics are using AI models to explain the intricate genetic data which provide valuable insights into disease mechanisms and patient treatment responses. AI-powered tools are processing large-scale medical data, decode biological pathways, and identify potential drug targets or biomarkers. AI algorithm improves the accuracy and speed of disease detection, which leads to early intervention and better patient diagnosis. Ai-driven models can be used to optimize treatment regimens, personalize therapies, and facilitate drug repurposing efforts in the therapeutics sector.
By End User
Pharmaceutical companies are anticipated to lead the generative AI in the biotech market
with the largest revenue of 39.6 % by the end of 2024 as they transform traditional pharmaceutical research and development practices by using an AI-driven model. Pharmaceuticals are fueling the growth of this market due to the inclusion of AI into their operations which allows firms to make substantial changes in some areas like drug discovery,
clinical trials, and personalized medicine, therefore increasing the production of new drugs in the market.
Additionally, generative AI facilitates the recreating of existing drugs to improve precision medicine initiatives that cater to evolving healthcare needs. Biotechnology startups are leveraging AI algorithms to explore different chemical spaces, design novel proteins, and analyze genomic data, accelerating their research and development efforts. These startups often introduce new and fresh AI applications and business models, which increases technological progress and market expansion of biotechnology. It helps them to make data-driven decision-making, improve research outcomes, and attract investment and partnerships.
Biotech startups use the agility and innovation offered by generative AI to compete with established players, contributing to the growth of the biotech industry. Academic and research organizations are prominent in advancing AI research which leads to the development of new tools and methodologies for biotech applications.
The Generative AI in Biotech Market Report is segmented based on the following:
By Technology
- Natural Language Processing (NLP)
- Generative Adversarial Networks (GANs)
- Vibrational Auto Encoders (VAEs)
- Reinforcement Learning
- Others
By Application
- Drug discovery
- Protein engineering
- Genomics
- Bioinformatics
- Others
By End User
- Pharmaceutical companies
- Biotechnology startups
- Academic institutions
- Research organizations
- Others
Regional Analysis
North America is expected to dominate the generative AI in the biotech market
with the largest revenue share of 39.1% in 2024. This region is leading due to strong investment, cutting-edge research institutions, and supportive regulatory frameworks. High emphasis on innovation in this region along with the presence of popular biotech and AI companies creates an environment for the advancement in generative AI applications within the biotechnology sector.
An advanced biotech ecosystem, substantial healthcare spending, and a culture that embraces technological progress are the main factors that are shaping this region. Ongoing investments in AI research, favorable regulatory environments, and collaborative initiatives between the technology and biotech industries are expected to continue driving the region's market share.
High emphasis on collaboration among researchers and industry and access to cutting-edge computing infrastructure, availability of vast funding opportunities in this region, collectively drive the expansion of the biotech market. Further, there is a huge pool of skilled professionals in data science, machine learning, and AI due to the availability of leading universities, research institutions, & tech hubs in this region. According to a report published by EY Biotechnology, public biotech companies in the US and Europe account for the revenue share of USD 215 billion in 2022 which shows this region's capacity to invest in innovation, even in challenging economic times.
By Region
North America
Europe
- Germany
- The U.K.
- France
- Italy
- Russia
- Spain
- Benelux
- Nordic
- Rest of Europe
Asia-Pacific
- China
- Japan
- South Korea
- India
- ANZ
- ASEAN
- Rest of Asia-Pacific
Latin America
- Brazil
- Mexico
- Argentina
- Colombia
- Rest of Latin America
Middle East & Africa
- Saudi Arabia
- UAE
- South Africa
- Israel
- Egypt
- Rest of MEA
Competitive Landscape
Key market players are playing a crucial role in driving innovation by leveraging artificial intelligence to expedite drug discovery, personalized medicine, and genetic research. Insilico Medicine and BenevolentAI are leaders in AI-driven drug development and disease comprehension, showcasing substantial market impact and strategic importance. This competitive and dynamic landscape reflects strategic initiatives and market influence shaping the future of biotech through AI integration.
Recursion Pharmaceuticals and Atomwise specialize in AI applications for drug repurposing and molecular predictions, respectively, indicating their vital roles in reducing development timelines and costs. Deep Genomics focuses on genetic disease research, while Ginkgo Bioworks and Zymergen highlight synthetic biology applications of AI. OpenAI and DeepMind, although broader in scope, contribute foundational AI advancements that support sector-specific applications. Other key players also bring valuable innovations, collectively propelling the market's progress.
Some of the prominent players in the global generative AI in biotech market are:
- Insilico Medicine
- Recursion Pharmaceuticals
- Atomwise,
- Deep Genomics
- BenevolentAI,
- Numerate
- Ginkgo Bioworks
- Zymergen
- OpenAI
- DeepMind
- Others
Recent Development
- In November 2023, Genentech announced a multi-year research collaboration with Nvidia to leverage generative AI for accelerating drug discovery. Genentech is integrating its proprietary algorithms with Nvidia's DGX cloud services in this partnership.
- In November 2023, Terray Therapeutics announced an investment from NVIDIA to enable Generative AI Design for Drug Discovery by using Terray’s massive experimental data to enable generative AI to design optimized small molecule therapeutics.
- In September 2023, Evozyne, a startup focusing on generative AI in biotech, raised USD 81.0 million in Series B funding, showcasing the increasing influence of generative AI in the biotech sector. Evozyne's algorithms, which emulate millions of years of protein evolution, aim to identify potential drug development candidates more efficiently than conventional methods.
- In June 2023, Insilico Medicine, a pioneering biotech firm specializing in generative AI, achieved a significant milestone with its AI-discovered and AI-generated drug, INS018_055. This potential anti-fibrotic drug candidate entered multi-regional Phase II clinical trials in both the U.S. and China, with initial patient doses administered.
- In December 2022, Terray Therapeutics and Nvidia announced two notable developments in the domain, introducing robust generative models named Chroma and RoseTTAFold Diffusion for precise protein designs not found in nature.
Report Details
| Report Characteristics |
| Market Size (2024) |
USD 90.5 Mn |
| Forecast Value (2033) |
USD 714.3 Mn |
| CAGR (2023-2032) |
25.8% |
| Historical Data |
2018 – 2023 |
| Forecast Data |
2024 – 2033 |
| Base Year |
2023 |
| Estimate Year |
2024 |
| Report Coverage |
Market Revenue Estimation, Market Dynamics, Competitive Landscape, Growth Factors and etc. |
| Segments Covered |
By Technology (Natural Language Processing, Generative Adversarial Networks , Vibrational Auto Encoders, Reinforcement Learning, and Others), By Application (Drug discovery, Protein engineering, Genomics, Bioinformatics, and Others ), By End Users (Pharmaceutical companies, Biotechnology startups, Academic institutions, Research organizations, and Others) |
| Regional Coverage |
North America – The US and Canada; Europe – Germany, The UK, France, Russia, Spain, Italy, Benelux, Nordic, & Rest of Europe; Asia- Pacific– China, Japan, South Korea, India, ANZ, ASEAN, Rest of APAC; Latin America – Brazil, Mexico, Argentina, Colombia, Rest of Latin America; Middle East & Africa – Saudi Arabia, UAE, South Africa, Turkey, Egypt, Israel, & Rest of MEA
|
| Prominent Players |
Insilico Medicine, Recursion Pharmaceuticals, Atomwise, Deep Genomics, BenevolentAI, Numerate, Ginkgo Bioworks, Zymergen, OpenAI, DeepMind, Other Key Players |
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