Global Generative AI in Biology Market, By Application (Medical Imaging,  Genomics & Proteomics, and Other), By Technology (Generative Adversarial Networks, Variational Autoencoders, Other), By End-User, By Region and Companies - Industry Segment Outlook, Market Assessment, Competition Scenario, Trends, and Forecast 2023-2032

Report Overview

Global Generative AI in Biology Market size is expected to be worth around USD 346.9 million by 2032 from USD 72.0 million in 2022, growing at a CAGR of 17.50 %. during the forecast period from 2023 to 2032.

Generative AI in biology refers to the application of artificial intelligence techniques, specifically generative models, to solve problems and generate new insights in the field of biology. Generative models are machine learning algorithms that can generate new data samples. In the context of biology, generative AI utilizes these models to generate new biological entities or simulate complex biological systems. It leverages the vast number of biological data available to generate novel molecules, proteins, and DNA sequences or even simulate biological processes.

By learning from existing biological data, generative AI algorithms can generate new instances that have the potential to be further explored and validated in biological research and applications. Generative AI in biology Market holds significant promises as it can expedite research, help identify potential solutions to biological challenges, and contribute to understanding complex biological systems.

Driving Factors

Computational Power and Infrastructure

The availability of powerful computing resources, including high-performance computing clusters and cloud-based infrastructure, has enabled the training of complex generative AI models. These Resources allow researchers and practitioners to process & analyze massive amounts of biological data, facilitating the development of advanced generative models.

Advancement and Development in Deep Learning and Generative Models

The rapid advancements in deep learning algorithms & generative models, such as variational Autoencoders (VAEs) & also generative adversarial networks (GANs), have paved the way for sophisticated generative AI applications in biology Market.

Accelerating Drug Discovery Process

The drug discovery process is costly, time-consuming, and often faces high failure rates. Generative AI in biology presents an opportunity to expedite the drug discovery pipeline by generating novel molecules, predicting drug-target interactions, and optimizing, attracting significant attention and investment in generative AI applications within the pharmaceutical and biotechnology industries.

Restraining Factors

Data Quality and Availability

Generative AI models require large and high-quality datasets for training. However, obtaining high-quality data can be challenging in biology due to various factors such as experimental limitations, data privacy concerns, and data sharing restrictions. Limited access to the drivers and well-curated Biological datasets can hinder the training and performance of generative AI models.

Lack of Computational Requirements and Infrastructure

Training and deploying complex generative AI models in biology often require significant computational resources and infrastructure. The computational demands can pose barriers, particularly for smaller research institutions or resource-constrained settings. Limited access to high-performance computing infrastructure and the associated costs can hinder the widespread adoption of generative AI in biology market.

Expertise and Training

Effectively harnessing the power of generative AI in biology requires expertise in both AI techniques and biological domain knowledge. Developing and deploying generative AI models necessitates interdisciplinary collaboration between biologists, computational scientists, and AI experts. The limited availability of individuals with expertise in both domains can hinder the widespread adoption and implementation of generative AI in biology Market.

COVID-19 Impact Analysis

The COVID-19 pandemic has significantly impacted various industries, including biology. The urgency to develop effective treatments and vaccines for COVID-19 has accelerated the application of generative AI in drug discovery. The COVID-19 pandemic has led to an increased demand for computational resources and infrastructure. The computational requirements for training and running complex generative AI models have grown as researchers in the field of biology have been leveraging AI techniques for various applications, including drug discovery, protein engineering, and molecular design.

Meeting these computational demands has become crucial to effectively implement generative AI in biology Market.The COVID-19 pandemic has disrupted global economies, leading to changes in research funding priorities. Some funding sources have been redirected towards COVID-19-related research, which may have affected the availability of funds for other areas, including generative AI in biology.

However, the increased emphasis on health and biotechnology research in response to the pandemic may also open up new funding opportunities in the long term. The pandemic has led to disruptions in research activities, including laboratory closures, restrictions on in-person experiments, & disruptions in the supply chain of research materials. These disruptions have affected biological data collection and limited access to experimental validations, which are essential for training and validating generative AI models.

By Application Analysis

Drug Discovery and Development is dominant in Application Segment of Generative AI in Biology Market

The application of generative AI in drug discovery and development has gained significant attention and investment. This application has the potential to be one of the dominant areas within the generative AI in biology market. AI-driven generative models have the potential to accelerate the drug discovery process by predicting molecular structures and screening vast chemical libraries. Genomics and proteomics have been crucial areas of research in the biological sciences. Integrating generative AI techniques can enhance data analysis, interpretation, and prediction in these fields.

The ability to understand genetic variations and analyze large-scale genomic and proteomic datasets using AI models has significant potential. This application has the potential to hold a considerable market share owing to its broad applicability in research as well as clinical settings. Generative AI techniques, such as image reconstruction, have shown promise in medical imaging applications. AI models can generate high-quality medical images, aid in diagnosis, and assist in developing personalized treatment plans.

By Technology Analysis

The Generative Adversarial Networks Segment is a significant share in Application Segment in Generative AI in Biology Market in 2022.

GANs are a popular technology in generative AI. They consist of two neural networks, a generator and a discriminator, which work together in a competitive process. GANs have been applied to tasks such as generating realistic images, drug discovery, protein structure prediction, and synthetic biology. VAEs are another commonly used technology in generative AI. They are probabilistic models that can learn the underlying distribution of input data and generate new samples.

VAEs have been applied to tasks such as generating molecular structures, drug design, and analyzing genomic data. While reinforcement learning is primarily associated with tasks in the field of artificial intelligence, it has also found applications in generative AI in biology. Reinforcement learning algorithms can be used to optimize biological systems, design optimal treatment plans, and simulate biological processes.

By End-User

The Pharmaceutical and Biotechnology Segment is dominated in End-User Segment in Generative AI in Biology Market in 2022.

Pharmaceutical and biotechnology companies have been prominent end users in this field. These companies extensively use generative AI techniques for drug discovery, designing new therapeutic molecules, optimizing drug development processes, and predicting molecular interactions. Research institutions, including universities and academic centers, are also significant end users of generative AI in biology Market.

They employ these technologies for various applications, such as protein structure prediction, genomics research, and synthetic biology. Healthcare providers, including hospitals and clinics, may utilize generative AI in biology for applications like disease diagnosis, personalized medicine, and treatment optimization. Diagnostic and imaging companies can benefit from generative AI to improve medical imaging analysis, develop novel diagnostic tools.

Key Market Segments

Based On Application

• Medical Imaging
• Genomics and Proteomics
• Drug Discovery and Development
• Protein Engineering
• Synthetic Biology
• Other Applications

Based On Technology

• Generative Adversarial Networks
• Variational Autoencoders
• Reinforcement Learning
• Other Technologies

Based On End-User

• Pharmaceutical and Biotechnology Companies
• Research Institutions
• Healthcare Provider
• Other End-Users

Growth Opportunity

Increasing Demand for AI-driven Solutions

The demand for AI-driven solutions in biology is growing rapidly. Generative AI models have the potential to revolutionize various aspects of biological research, drug discovery, biomolecular design, & personalized medicine. As the need for efficient as well as innovative solutions in biology continues to rise, the market for intelligent AI within biology could be set to see significant growth.

Advancements in Machine Learning and AI Technologies

The continuous advancements in machine learning algorithms, deep learning techniques, & AI technologies are driving the growth of generative AI in biology Market. Researchers are constantly developing as well as refining generative models that can generate novel biological entities, simulate complex biological systems, & optimize biological processes. These advancements unlock new possibilities and expand the applications of generative AI in biology.

Trends

Advancements in Generative Adversarial Networks

GANs, a type of generative AI model, have gained significant attention in the field of biology. Researchers are exploring different variations & architectures of GANs to generate realistic as well as high-quality biological data, like synthetic images of cells, tissues, or molecules. Improvements in GANs are enabling more accurate and diverse generative capabilities.

Transfer Learning and Pertained Models

Transfer learning, a technique that leverages knowledge from pre-trained models on large datasets, is gaining prominence in generative AI in biology Market. Pertained models can be fine-tuned or used as a starting point for specific biological tasks, saving computational resources and reducing the need for large annotated datasets.

Regional Analysis

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

North America has been a leading region in developing and adopting generative AI in biology. The presence of established biotechnology and pharmaceutical companies, academic institutions, and research organizations, coupled with significant investments in AI research, has contributed to the growth of this market in the region.

Europe has also been a significant player in the generative AI in biology market.Countries such as the United Kingdom, Germany, France, and Switzerland have a strong presence of biotech companies and research institutions that have been actively leveraging generative AI techniques for various biological applications. The Asia Pacific region has witnessed rapid growth in the generative AI in biology market.

Countries like China, Japan, and South Korea have made significant investments in AI research and development, leading to the emergence of innovative applications in the field of biology. The presence of large populations and rising healthcare needs also contribute to the market's growth in this region.

Key Regions

• North America
  • The 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 leading player in the field of generative AI in biology. They specialize in using AI for drug discovery and aging research. The company has developed proprietary deep-learning techniques to generate novel molecules and predict their properties. They have collaborations with major pharmaceutical companies and academic institutions. DeepMind, a subsidiary of Alphabet Inc. (Google's parent company), has been actively involved in leveraging generative AI techniques in various scientific domains, including biology. They have developed AlphaFold, an AI system that accurately predicts protein structures. AlphaFold has made significant contributions to the field of structural biology and has gained widespread recognition.

Market Key Players

• NVIDIA Corporation
• IBM Corporation
• BenevolentAI
• DeepMind Technologies Limited
• Insilico Medicine
• Recursion Pharmaceuticals
• Zymergen
• Other Key Players

Recent Development

• Jun 15, 2023: For processing various data sets, Insilico has introduced a new multimodal transformer-based aging clock.

Report Scope