Generative AI in Science Market, By Deployment Model (Cloud-based, On-premises), By Application (Drug Discovery, Material Science, Medical Imaging and Healthcare, and Others), By End-User, By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032

Report Overview

Generative AI in Science Market size is expected to be worth around USD 45.9 Bn by 2032 from USD 3.2 Bn in 2022, growing at a CAGR of 31.4% during the forecast period from 2023 to 2032.

The global generative AI in science market is an emerging sector combining artificial intelligence (AI) and generative modeling techniques to revolutionize scientific research and discovery. Generative AI involves training AI models to generate new data, models, or simulations that are similar to the input data on which they were trained. This technology holds immense potential in various scientific fields, including drug discovery, material science, image and signal processing, natural language Processing(NLP), and simulation and modeling.

Generative AI has the ability to accelerate and enhance scientific research by automating tasks that were traditionally time-consuming and resource-intensive. In drug discovery, for example, generative AI models can generate novel molecules with specific properties, significantly speeding up the process of finding potential drug candidates. Similarly, in material science, generative AI can assist in the design of new materials with desired characteristics, such as improved strength or conductivity.

Driving factors

Advancements in Artificial Intelligence

The continuous advancements in artificial intelligence, including deep learning and generative modeling techniques, have expanded the capabilities of generative AI in science. Improved algorithms and models enable the generation of more accurate and realistic outputs, leading to increased adoption.

Accelerating Research and Development

Generative AI techniques offer the potential to significantly speed up the research and development process in various scientific domains. By automating tasks such as molecule generation, material design, and data synthesis, generative AI enables scientists to explore a larger design space and discover novel insights more rapidly.

Improved Drug Discovery

Generative AI plays a crucial role in accelerating drug discovery processes. By generating new molecules with desired properties, it assists in identifying potential drug candidates, reducing the time and cost involved in traditional drug development approaches. This efficiency gain has attracted significant interest from pharmaceutical intermediates companies and researchers.

Restraining Factors

Data Limitations, Computational Resources, and Ethical & Legal Considerations Restraining the Growth of Market

Data Limitations

Generative AI models rely on large and diverse datasets for training. However, in some scientific domains, obtaining sufficient high-quality data can be challenging due to limited availability, data privacy concerns, or the cost associated with data collection. Insufficient or biased data can lead to limitations in the generative capabilities of AI models.

Computational Resources

Training and deploying generative AI models require substantial computational resources, including high-performance computing & specialized hardware like graphics processing units as well as tensor processing units. The cost and availability of these resources may be a challenge for smaller research institutions or organizations with limited budgets.

Ethical and Legal Considerations

The use of generative AI in science raises ethical as well as legal concerns. Generating synthetic data or simulations may raise issues related to data ownership, intellectual property rights, and potential misuse of AI-generated content. Ensuring ethical guidelines and legal frameworks for responsible AI usage in scientific research is crucial.

Covid-19 Impact on Generative AI in Science Market

The COVID-19 pandemic has positively and negatively impacted the global generative AI market in science. Here are some ways in which COVID-19 has affected generative AI in Science:

Accelerated Drug Discovery, Generative AI techniques, can expedite the process of identifying potential drug candidates by generating novel molecules with desired properties. This can help in the search for effective treatments against COVID-19 and other diseases, potentially saving time and resources. Vaccine Design Optimization Generative AI can assist in designing optimized vaccine candidates by simulating the interaction between viral proteins and the immune system.

This can aid in developing vaccines that generate a robust immune response, potentially leading to more effective immunization strategies. Data Analysis and Insights Generative AI can analyze large datasets related to COVID-19, such as genomic, clinical, and epidemiological data, to identify patterns, generate insights, and make predictions. This can enhance our understanding of the virus, its transmission dynamics, and potential intervention strategies. While generative AI holds promise, practical applications may require further validation and rigorous testing.

Overreliance on AI-generated outputs without proper verification could lead to false conclusions or ineffective interventions. Resource Requirements, The deployment and maintenance of generative AI systems can be resource-intensive, requiring significant computational power and expertise. These requirements may pose challenges, particularly in resource-constrained settings or for researchers with limited access to such resources.

By Deployment Model Analysis

The Cloud-based Segment is dominated the Deployment model Segment in Generative AI in Science Market.

Based on the deployment model, the dominant deployment model tends to be cloud-based rather than on-premises. There are several reasons for cloud-based deployment being more prevalent in the context of generative AI in science, Scalability, Resource Availability, Accessibility, and Cost Efficiency.

Cloud-based deployment refers to running the AI models as well as algorithms on remote servers provided by cloud service providers, while on-premises deployment involves running the models on local infrastructure within an organization's premises. These situations typically arise when organizations have specific data privacy or security requirements that require keeping data and AI processes within their infrastructure. Certain regulations or sensitive data types may require stricter data storage technologies and processing control, leading to an on-premises deployment approach.

By Application Analysis

Drug Discovery is dominated in the Application Segment in Generative AI in Science Market.

Generative AI has gained significant traction in the field of drug discovery. The ability of generative AI models to design and optimize molecules for specific targets can potentially revolutionize the process of developing new therapeutics. Drug discovery is one of the prominent areas where generative AI has seen substantial research and investment. Generative AI has also made notable strides in material science.

The ability to generate new materials with desired properties and optimize their characteristics has the potential to accelerate material discovery and innovation. While still emerging, generative AI applications in material science have shown promising results. Generative AI techniques have been increasingly applied in medical imaging and healthcare applications. These techniques can generate synthetic medical images, aid in image reconstruction, segmentation, and analysis, and support clinical decision-making. The healthcare industry has been actively exploring and adopting generative AI technologies.

By End-User Analysis

The Pharmaceutical and Biotechnology companies are dominated in End-User Segment in Generative AI in Science Market.

Pharmaceutical and Biotechnology Companies' generative AI has gained significant traction in pharmaceutical and biotechnology companies. These organizations often have dedicated research and development teams focused on drug discovery, optimization, and molecular design. These companies often have the necessary resources & infrastructure to invest in AI research and development. As a result, they have been at the forefront of adopting as well as applying generative AI techniques to accelerate drug development. Research institutions and academic institutions play a crucial role in advancing scientific knowledge & conducting innovative research.

Generative AI is increasingly being utilized in these institutions across various scientific disciplines. Researchers and academics are exploring applications of generative AI in fields like material science, molecular biology, astrophysics, and more. While generative AI may not be as dominant in healthcare providers compared to pharmaceutical companies and research institutions, this sector has emerging applications. Healthcare providers are exploring the use of generative AI in medical imaging, clinical decision support systems, personalized medicine, & patient monitoring. As the healthcare industry continues to adopt AI technologies, the use of generative AI in healthcare is expected to grow.

Key Market Segments

Based on the Deployment Model

• Cloud-based
• On-Premises

Based on Application

• Drug Discovery
• Material Science
• Medical Imaging and Healthcare
• Astrophysics and Astronomy
• Molecular Biology
• Other Applications

Based on End-User

• Pharmaceutical and Biotechnology companies
• Research Institutions and Academic Institutions
• Healthcare Providers
• Government Organizations
• Other End-Users

Growth Opportunity

Generative AI has indeed emerged as a significant area of growth as well as an opportunity in the field of science. Generative AI refers to using machine learning techniques to generate new data, content, and solutions based on patterns. Generative AI has the potential to revolutionize various domains, including drug discovery, materials science, image synthesis, & molecular design, among others. Generative AI can accelerate the drug discovery process by generating novel molecules with desired properties, potentially leading to the development of more effective and safer drugs. By training models on large databases of molecular structures and properties, generative AI can propose new chemical compounds for further exploration.

Latest Trends

Increased Adoption in Pharmaceutical Industry: The pharmaceutical industry has shown a growing interest in leveraging generative AI for drug discovery and development. Generative AI models can assist in identifying potential drug candidates, optimizing molecular structures, and predicting drug properties, leading to more efficient and cost-effective drug development processes.

Expansion into Material Science: Generative AI techniques are gaining traction in material science. Researchers use generative models to design new materials with desired properties, optimize material structures, and accelerate the discovery of materials. This trend has the potential to impact various industries, including aerospace.

Regional Analysis

North America Accounted for the Largest Share of Generative AI in the Science Market in 2022.

North America, particularly the United States, has been a leader in generative AI applications in science. Prominent research institutions, such as Stanford University, Massachusetts Institute of Technology (MIT), and University of California, Berkeley, have been at the forefront of exploring generative models in scientific domains. Many technology companies in Silicon Valley, & other major tech hubs have also been actively working on generative AI applications in science, ranging from drug discovery & material design to climate modeling and genomics.

Europe has also seen significant contributions to generative AI in science. Research institutions and universities across countries like the UK, Germany, and France have been conducting cutting-edge research in various scientific disciplines. The European Union has been investing in AI research and development through initiatives like the European AI Fund as well as European Open Science Cloud, which aim to collaborate & innovate in generative AI.

Key Regions and Countries

North America

• US
• Canada
• Mexico

Western Europe

• Germany
• France
• The UK
• Spain
• Italy
• Portugal
• Ireland
• Austria
• Switzerland
• Benelux
• Nordic
• Rest of Western Europe

Eastern Europe

• Russia
• Poland
• The Czech Republic
• Greece
• Rest of Eastern Europe

APAC

• China
• Japan
• South Korea
• India
• Australia & New Zealand
• Indonesia
• Malaysia
• Philippines
• Singapore
• Thailand
• Vietnam
• Rest of APAC

Latin America

• Brazil
• Colombia
• Chile
• Argentina
• Costa Rica
• Rest of Latin America

Middle East & Africa

• Algeria
• Egypt
• Israel
• Kuwait
• Nigeria
• Saudi Arabia
• South Africa
• Turkey
• United Arab Emirates
• Rest of MEA

Market Share & Key Players Analysis

Insilico Medicine is a company that utilizes generative AI techniques for drug discovery and development. They employ deep learning models and reinforcement learning algorithms to accelerate the drug discovery process. Insilico Medicine aims to identify new drug candidates and optimize drug design using generative AI approaches. NVIDIA has been a leading player in the AI industry and has made significant contributions to generative AI. Their GPUs (Graphics Processing Units) have been widely used for training and running AI models, including generative models. While NVIDIA's primary focus is on providing hardware solutions, its technology has played a crucial role in advancing generative AI in science.

Top Key Players in Generative AI in Science Market

• NVIDIA
• Insilico Medicine
• Atomwise
• Recursion Pharmaceuticals
• Intel
• Yseop
• BenevolentAI
• Other Key Players

Recent Development

Report Scope: