AI in Synthetic Biology Market Size, Share, Growth 2024 To 2033

Artificial Intelligence (AI) in Synthetic Biology Market Size and Growth 2024 to 2033

The global artificial intelligence (AI) in synthetic biology market size was valued at USD 81 million in 2023 and is anticipated to reach around USD 387.62 million by 2033, growing at a CAGR of 16.94% from 2024 to 2033.

The artificial intelligence (AI) in synthetic biology market relates to applying AI in enhancing efficiency in designing, engineering and optimizing biology systems. This market is for application of AI in gene editing, metabolic engineering, drug discovery and development of synthetic creatures. AI in synthetic biology market is growing at fast pace as AI becomes an indispensable component in the synthetic biology. The appreciation of AI in analyzing large sets of data, identifying patterns and predicting results augments the capabilities in designing genetic circuits, metabolic routes and improving biological productivity which are very much complex and time taking when addressed in conventional means. In synthetic biology, AI is being used to put together unique applications in the betterment of human health, clean energy and environmental remediation. The expansion of the synthetic biology market is inevitable as it has been identified as a vital factor in enhancing the efficiency and advancement of biological products through the integration of artificial Intelligence technology that is vital in the creation of more sophisticated bio-products that are tailored to suit industries across the globe.

• According to the European Commission (EC), the EU has established a legal framework to ensure that the development of modern biotechnology, and more specifically of GMOs, takes place in safe conditions. These efforts are crucial in ensuring the responsible development and deployment of synthetic biology applications, safeguarding both the environment and public health.
• According to IEA, global growth in electricity demand eased slightly to 2.2% in 2023 due to falling electricity consumption in advanced economies, it is projected to accelerate to an average of 3.4% from 2024 through 2026. About 85% of the increase in the world’s electricity demand through 2026 is expected to come from outside advanced economies – most notably China, India, and countries in Southeast Asia.
• In June 2023, the UK Research Council established the Synthetic Biology for Growth Programme following recommendations on synthetic biology research and investment from the UK government. The total budget allotted by the UK government for the research is around $108.1 million (€102 million).

Report Highlights

• North America was leading revenue share holder in 2023 (38%).
• Europe region held second highest position in 2023 (31%).
• Asia-Pacific region is experiencing repid growth in upcoming years.

Artificial Intelligence (AI) in Synthetic Biology Market Growth Factors

• Government Policies and Support: As suggested by the above post, governments all over the world are making positive policies and funding to support synthetic biology. This area is spurred by endeavours in enhancing healthcare delivery through artificial intelligence, sustainable bio-production, and environmental management. They are in form of grants and subsidies and supportive policies that are promoting its use and application in this creative field.
• Advancements in Technology: These advancements in AI and ML are augmenting synthetic biology in one continuous innovation after another. AI is thus helping with design, modelling, and simulation of biological systems making the design and development of new biological products faster and cheaper to fashion. Advancements in computing, in laboratories and genetic engineering are also helping cut costs as well as increase efficiency and reliability.
• Integration with Renewable Energy: The growing application of AI in healthcare and pharmaceuticals is a major growth driver for synthetic biology. AI-driven tools are enabling the rapid development of novel therapeutics, personalized medicine, and advanced diagnostic tools. These technologies are particularly beneficial in drug discovery and development, where AI can significantly reduce the time and cost associated with bringing new treatments to market.
• Decarbonization Initiatives: Global efforts towards decarbonization are driving the adoption of Artificial Intelligence (AI) for Synthetic Biology technologies. As industries and economies aim to reduce their carbon footprints, smart grids, and energy management systems are emerging as key components in the transition to cleaner energy systems. These technologies are especially crucial in sectors where energy consumption is high and efficiency gains are most needed.
• Sustainability Initiatives: Global efforts toward sustainability are accelerating the adoption of AI in synthetic biology. As industries and economies shift towards greener and more sustainable practices, AI-powered synthetic biology is emerging as a key solution for developing bio-based products and processes. These technologies are crucial for sectors focused on reducing their environmental impact and improving the efficiency of resource use.
• Strategic Partnerships and Investments: The market is witnessing a rise in collaborations between biotech companies, AI technology providers, and research institutions. These partnerships are fostering the development of AI-driven synthetic biology solutions, with significant investments in bioengineering, data analytics, and AI research driving market growth and innovation.
• Biological Security and Innovation: AI in synthetic biology is enhancing biological security by enabling the design of more resilient and adaptable biological systems. These technologies allow for the development of bio-based solutions that can address global challenges, such as pandemics and food security, by providing reliable and scalable methods for biomanufacturing and bioengineering.

Artificial Intelligence (AI) in Synthetic Biology Market Trends

• Rise of Computational Biology: A notable trend is the increasing use of AI-driven computational biology tools. These tools allow for the real-time modelling, simulation, and optimization of biological systems, improving the precision and efficiency of synthetic biology research and development.
  Expansion of Bio-Manufacturing: The concept of AI-driven bio-manufacturing is gaining momentum, where AI optimizes the production of bio-based materials and chemicals. This approach enhances the scalability and sustainability of synthetic biology applications, particularly in sectors like agriculture, chemicals, and biofuels.
• Personalized Medicine and Therapeutics: AI is driving the development of personalized medicine, with synthetic biology playing a crucial role in creating customized treatments. AI-driven platforms are enabling the rapid design and testing of personalized therapies, which is revolutionizing healthcare by providing tailored solutions for individual patients.
• Collaborations and Joint Ventures: The market is seeing a surge in collaborations and joint ventures among stakeholders. Biotech firms, AI companies, and academic institutions are partnering to advance synthetic biology technologies. These collaborations aim to combine expertise, share resources, and accelerate the commercialization of AI-driven synthetic biology solutions.
• Focus on Ethical Standards and Biosafety: As AI-driven synthetic biology technologies advance, there is a growing emphasis on ethical standards and biosafety. Ensuring the safe and responsible use of these technologies is critical for gaining public trust and regulatory approval. Organizations are working towards establishing comprehensive guidelines and standards to mitigate risks and ensure the safe application of AI in synthetic biology.

Report Scope

Artificial Intelligence (AI) in Synthetic Biology Market Dynamics

Drivers

• Advancement in AI Technologies: AI is significantly enhancing the capabilities of synthetic biology, particularly in the areas of gene editing, metabolic engineering, and bioinformatics. These advancements are driving the market forward by enabling more efficient and precise biological research and development.
• Growing Demand for Sustainable Solutions: The increasing demand for sustainable bio-based products is a major driver for AI in synthetic biology. These technologies support the development of environmentally friendly alternatives to traditional products, which is crucial in addressing global sustainability challenges.

Restraints

• High Initial Costs: Implementing AI-driven synthetic biology technologies requires substantial initial investment in research, equipment, and digital infrastructure. The high costs associated with these technologies can limit their widespread adoption, particularly in smaller companies and emerging markets.
• Technical and Ethical Challenges: The complexity of synthetic biology, combined with the rapid pace of AI development, poses significant technical and ethical challenges. Addressing concerns related to biosafety, biosecurity, and the ethical implications of genetic engineering is essential for the responsible growth of this market.

Opportunities

• Integration with Healthcare and Pharma: The increasing application of AI in healthcare offers substantial opportunities for synthetic biology. AI-driven technologies can enhance drug discovery, personalized medicine, and the development of innovative therapies, providing significant growth potential for the market.
• Government and Private Sector Investments: Rising investments from governments and private sectors in AI and synthetic biology research present significant growth opportunities. Financial incentives, public-private partnerships, and dedicated research funding are driving innovation and accelerating the adoption of AI in synthetic biology.

Challenges

• Regulatory Hurdles: The evolving regulatory landscape and the complexity of synthetic biology pose challenges for market growth. Clear and supportive regulatory frameworks are essential to facilitate the safe and effective application of AI in synthetic biology.
• Technological Maturity: While AI-driven synthetic biology technologies are advancing rapidly, achieving full technological maturity remains a challenge. Ongoing research and development are needed to ensure these technologies are reliable, scalable, and cost-effective.

Artificial Intelligence (AI) in Synthetic Biology Market Segmental Analysis

Application Analysis

Drug Discovery and Development: AI is revolutionizing drug discovery and development by identifying potential drug candidates, optimizing chemical compounds, and predicting therapeutic efficacy. Machine learning algorithms can analyse vast datasets of molecular structures and biological interactions, significantly reducing the time and cost required to bring new drugs to market. Subsegments include AI-driven molecular docking, compound screening, and predictive models for assessing drug toxicity and efficacy.

Genomic Analysis: In genomic analysis, AI plays a critical role in deciphering complex genetic data, identifying genetic variations, and pinpointing gene-editing targets. AI tools are used to detect patterns and correlations in large genomic datasets, aiding in the understanding of disease mechanisms and the identification of potential therapeutic targets. Subsegments include AI-assisted genome-wide association studies (GWAS), identification of single nucleotide polymorphisms (SNPs), and AI-enhanced CRISPR target site selection.

Metabolic Engineering: AI optimizes metabolic pathways in microorganisms to enhance the production of biofuels, chemicals, and pharmaceuticals. By modelling and simulating complex biochemical networks, AI helps in designing efficient metabolic circuits that maximize yield and reduce by-products. Subsegments include AI-driven pathway optimization, strain engineering for industrial biotechnology, and the prediction of metabolic flux distributions.

Protein Engineering: AI applications in protein engineering involve designing and optimizing proteins with specific functions or properties for industrial and therapeutic uses. AI can predict protein folding, engineer enzyme activity, and design novel proteins that perform specific tasks, such as catalysing reactions or binding to targets. Subsegments include AI-based protein structure prediction, enzyme engineering for biocatalysis, and therapeutic protein design.

Synthetic Genomics: AI is used in synthetic genomics to design and construct new genomes, enabling the creation of novel biological systems. This involves using AI to predict the functions of genes and regulatory elements, guiding the synthesis of entire genomes tailored for specific applications, such as bio-production or environmental remediation. Subsegments include AI-driven genome design, synthetic chromosome construction, and the development of minimal genomes for synthetic organisms.

Technology Analysis

Machine Learning: Machine learning is a cornerstone technology in synthetic biology, enabling systems to learn from data and improve their predictions and analyses over time. It is widely used across various applications, from predicting gene expression patterns to optimizing metabolic pathways and drug discovery processes. Subsegments include supervised learning for gene function prediction, unsupervised learning for clustering biological data, and reinforcement learning for optimizing experimental designs.

Natural Language Processing (NLP): NLP technologies facilitate the extraction and analysis of information from scientific literature and databases related to synthetic biology. AI-driven NLP can process vast amounts of text to identify relevant research findings, extract key insights, and support knowledge discovery. Subsegments include AI-based literature mining for gene-disease associations, automated extraction of experimental protocols, and sentiment analysis in scientific publications.

Computer Vision: Computer vision systems analyse and interpret visual data, playing a crucial role in high-throughput screening and phenotypic analysis of biological samples. AI-driven image analysis can identify cellular structures, monitor microbial growth, and detect morphological changes, enabling faster and more accurate biological assessments. Subsegments include AI-powered microscopy image analysis, automated phenotyping of genetically engineered organisms, and real-time monitoring of bioprocesses through video analysis.

Robotics and Automation: AI-driven robotic systems automate laboratory processes, including sample preparation, DNA synthesis, and high-throughput experimentation. Robotics combined with AI enhances the precision, speed, and scalability of synthetic biology research, allowing for more complex and reproducible experiments. Subsegments include AI-controlled liquid handling systems, automated colony picking and screening, and integration of AI with laboratory information management systems (LIMS).

Deployment Mode Analysis

Cloud-based Solutions: Cloud-based AI applications offer scalability and flexibility for synthetic biology research and development. These solutions allow researchers to access powerful computational resources, collaborate across different locations, and scale their operations without investing in extensive on-premises infrastructure. Subsegments include AI platforms for cloud-based genomic analysis, collaborative cloud environments for synthetic biology research, and cloud-hosted AI tools for drug discovery.

On-Premises Solutions: On-premises AI systems are deployed within an organization’s infrastructure, providing enhanced control and security over data and processes. These solutions are often preferred by institutions with sensitive data or those requiring high-performance computing resources directly within their facilities. Subsegments include AI-driven on-premises bioinformatics pipelines, in-house AI tools for metabolic engineering, and secure on-premises AI platforms for pharmaceutical development.

Artificial Intelligence (AI) in Synthetic Biology Market Regional Analysis

The AI in synthetic biology market is segmented by region, including North America, Europe, Asia-Pacific, and LAMEA. Below is a detailed overview of each region:

Why is North America dominating the AI in synthetic biology market?

The North America AI in synthetic biology market size was estimated at USD 30.78 million in 2023 and is expected to reach around USD 147.30 million by 2033. North America is rapidly advancing, driven by significant investments in biotechnology and AI research. The United States and Canada lead in the development of AI-driven tools for gene editing, drug discovery, and metabolic engineering. This region benefits from strong government support, a robust biotech ecosystem, and collaborations between academic institutions and industry players. Efforts in North America are focused on leveraging AI to accelerate innovation in synthetic biology, particularly in the areas of personalized medicine and bio-manufacturing.

Europe AI in Synthetic Biology Market Tends

The Europe AI in synthetic biology market size was accounted for USD 25.11 million in 2023 and is expected to reach around USD 120.16 million by 2033. Europe is a key player with strong emphasis on ethical research practices and regulatory compliance. Countries like Germany, France, and the UK are at the forefront of integrating AI technologies into synthetic biology, particularly in areas such as genomic analysis, protein engineering, and synthetic genomics. The European market is characterized by substantial public and private investments, as well as strategic partnerships aimed at advancing AI applications in synthetic biology. The region's commitment to sustainability and innovation drives the development of AI tools that support environmentally friendly and socially responsible biotech solutions.

Why is Asia-Pacific experiencing rapid growth?

The Asia Pacific AI in synthetic biology market size was worth USD 19.44 million in 2023 and is projected to surpass around USD 93.03 million by 2033. The Asia-Pacific region is experiencing rapid growth in the AI in synthetic biology market, fueled by extensive investments in AI technology and biotech infrastructure. China, Japan, and South Korea are leading in the adoption of AI for synthetic biology applications, including drug development, metabolic engineering, and bioinformatics. The region's focus on innovation and technological advancement is supported by government initiatives and increasing private sector involvement. Asia-Pacific's dynamic market environment fosters the development of AI-driven synthetic biology solutions that address both regional and global challenges, such as healthcare innovation and sustainable agriculture.

Latin America, Middle East, and Africa Market Trends

The LAMEA AI in synthetic biology market size was valued at USD 5.67 million in 2023 and is expected to reach around USD 27.13 million by 2033. LAMEA region is emerging, with growing interest in adopting AI-driven biotech solutions to address unique regional challenges. In Latin America, countries like Brazil and Argentina are exploring AI applications in synthetic biology to enhance agricultural productivity and develop bio-based industries. The Middle East is investing in AI technologies to diversify its economy and enhance healthcare innovation, while Africa is beginning to embrace AI for genomic analysis and bio-manufacturing. Despite economic and infrastructure challenges, the LAMEA region holds significant potential for growth, driven by abundant natural resources and a rising focus on sustainable development and technological advancement.

Artificial Intelligence (AI) in Synthetic Biology Market Top Companies

• IBM Corporation
• Microsoft Corporation
• Google DeepMind
• Benevolent AI
• Insilico Medicine
• Zymergen
• Ginkgo Bioworks
• Synthace
• Recursion Pharmaceuticals
• Berkeley Lights
• Twist Bioscience
• Atomwise
• XtalPi
• AbCellera
• Arzeda

The AI in synthetic biology market is characterized by a dynamic mix of key players, including leading biotechnology firms, pharmaceutical companies, and technology giants. Companies like Ginkgo Bioworks, Inscripta, and Zymergen are at the forefront, leveraging AI to advance synthetic biology applications such as gene editing, metabolic engineering, and protein design. Major pharmaceutical companies like Pfizer and Novartis are increasingly adopting AI-driven synthetic biology for drug discovery and development. Meanwhile, technology leaders such as IBM and Google are providing AI platforms and computational tools that enable breakthroughs in genomic analysis and bioinformatics. The collaborative efforts between these diverse players are driving innovation, accelerating research, and pushing the boundaries of what is possible in synthetic biology.

CEO Statements

Here are some recent CEO statements from market players in the AI in synthetic biology market:

Jason Kelly, CEO of Ginkgo Bioworks

"Jason Kelly emphasized the transformative potential of AI in synthetic biology, particularly through Ginkgo's partnership with Google Cloud. He stated that the collaboration is set to "reshape humanity's understanding of biology" by applying AI to genomics, protein studies, and other areas critical to biotech. This partnership aims to develop advanced AI models that will enhance drug discovery and biosecurity efforts, leveraging the power of AI to push the boundaries of what’s possible in synthetic biology."

Miriam Fernández, S&P Global

"In a recent report, Fernández highlighted how AI is central to the rapid advancements in synthetic biology. She pointed out that the integration of AI with engineering and biological sciences is creating new opportunities across various industries, including healthcare, agriculture, and renewable energy. She also touched on the ethical considerations that come with these advancements, noting that while AI enables unprecedented innovation, it also necessitates careful risk management to avoid unintended consequences."

These statements reflect the commitment of key industry players to advancing AI in synthetic biology technologies and supporting the global transition to sustainable energy solutions.

Recent Developments

Strategic partnerships and Launches highlight the rapid advancements and collaborative efforts in the AI for synthetic biology market. Industry players are involved in various aspects of AI for synthetic biology, including production, storage technologies, and fuel cells, and play a significant role in advancing the market. Some notable examples of key developments in the market include:

• In September 2023, Merck, a science, and technology company, announced two new strategic drug discovery collaborations aimed at harnessing powerful artificial intelligence (AI)-driven design and discovery capabilities, further advancing the company’s research efforts.
• In August 2023, GenScript Biotech, the life-science research tools and services provider, and T-MAXIMUM Biotech, a biotechnology company pioneering universal cell therapies, formed a strategic collaboration agreement to enable the development of T-MAXIMUM’s development of CAR-T cell therapy using GenScript’s CRISPR nucleic acid reagents.
• In November 2022, BigHat BioSciences, Inc., a leading biotechnological company utilizing ML-guided antibody discovery and development platforms, collaborated with Merck to develop an AI-enabled platform for expediting protein engineering and designing novel therapeutic candidates. This collaboration aims to optimize up to 3 proteins by leveraging BigHat’s platform for synthesizing, expressing, purifying, and characterizing molecules. Such partnerships highlight the growing significance of AI and ML in revolutionizing the field of synthetic biology.
• In July 2022, SynbiCITE, the UK's National Center for the Industrial Translation of Synthetic Biology, secured a substantial funding commitment of $5.7 million from SynBioVen, a venture capital investment firm based in the UK. This investment aims to bolster synthetic biology startups and small to medium-sized enterprises (SMEs), particularly in the healthcare and biotechnology sectors. These developments underscore significant strides in advancing hydrogen infrastructure and technology, reflecting growing collaborations and strategic investments aimed at expanding the global hydrogen economy.

Market Segmentation

By Application

• Drug Discovery and Development
• Genomic Analysis 
• Metabolic Engineering
• Protein Engineering
• Synthetic Genomics

By Technology

• Machine Learning
• Natural Language Processing (NLP)
• Computer Vision
• Robotics and Automation

By Deployment Mode

• Cloud-based Solutions
• On-Premises Solutions

By Regions

• North America
• APAC
• Europe
• LAMEA