AI in Bioinformatics Market Projected to Surge to USD 136.3 Million by 2033

Overview

The Global AI In Bioinformatics Market size is expected to be worth around USD 136.3 Million by 2033, from USD 3.8 Million in 2023, growing at a CAGR of 42.9% during the forecast period from 2024 to 2033.

The global AI in bioinformatics market is gaining strong momentum as artificial intelligence transforms the way biological and genomic data are analyzed. Growing volumes of sequencing data, rising demand for precision medicine, and increasing use of AI in drug discovery are encouraging healthcare organizations, research institutes, and biotechnology companies to adopt advanced bioinformatics platforms.

AI-powered algorithms help researchers identify disease-related genes, predict protein structures, improve biomarker discovery, and accelerate clinical research while reducing analysis time. Government agencies and healthcare institutions are also supporting responsible AI adoption through research initiatives, data-sharing frameworks, and regulatory guidance.

The National Institutes of Health (NIH) continues to expand programs such as Bridge2AI to develop high-quality datasets for biomedical AI applications, while also promoting secure and ethical use of genomic information. The U.S. Food and Drug Administration (FDA) has introduced a risk-based framework to improve the credibility of AI models used in drug and biological product development, strengthening confidence in AI-enabled research.

In 2025, NIH researchers introduced GeneAgent, an AI system designed to improve the accuracy of gene set analysis by validating results against expert-curated biological databases, supporting faster interpretation of complex molecular data.

Additionally, the FDA reported that the use of AI in regulatory submissions for drug development has grown exponentially since 2016, reflecting the expanding role of AI across life sciences and bioinformatics. These developments are expected to accelerate innovation, improve research efficiency, and strengthen the future of AI-driven bioinformatics solutions.

Key Takeaways

• The analysis services segment dominated the AI in bioinformatics market, accounting for 57.8% of the total market share in 2023.
• The machine learning segment led the market by type, capturing 49.3% of the global AI in bioinformatics market share in 2023.
• Among product and service categories, the bioinformatics services segment held the largest share, representing 61.5% of the market in 2023.
• Based on application, the genomics segment emerged as the leading category with a 27.7% market share in 2023, driven by its broad adoption and scalability.
• By sector, the medical biotechnology segment secured the leading position, accounting for 38.4% of the global market revenue in 2023.
• North America led the global AI in bioinformatics market in 2023, capturing 46.5% of the market and generating approximately USD 1.7 million in revenue.

Statistical Information

• The NIH Bridge2AI program initially funded 24 research projects to develop AI-ready biomedical datasets, standards, and tools that support machine learning applications in bioinformatics and precision medicine.
• In January 2026, the NIH approved Bridge2AI Stage 2, building on four years of datasets, AI tools, and best practices to accelerate biomedical research using artificial intelligence.
• The NIH Bridge2AI Training, Recruitment, and Mentoring initiative has engaged more than 30 scholars to strengthen the biomedical AI and bioinformatics workforce across North America.
• The same NIH initiative includes over 100 mentors, providing interdisciplinary guidance in AI, genomics, bioinformatics, and biomedical research.
• Google’s AlphaFold AI model was trained using approximately 200,000 experimentally determined protein structures from the Protein Data Bank, significantly advancing AI-driven structural biology.
• A recent NIH-funded analysis examined 1,699 biomedical dataset publications to identify characteristics associated with successful AI-ready datasets and stronger clinical translation.
• In 2026, the NIH announced a new Bridge2AI funding opportunity with 5 expected awards, each supporting continued development of trusted AI resources for biomedical and bioinformatics research.
• The NIH funding opportunity provides awards of up to USD 7 million per project to advance AI-enabled biomedical research, including the development of high-quality datasets and AI frameworks for life sciences.

Regional Analysis

North America dominated the global AI in bioinformatics market in 2023, accounting for 46.5% of the total market and generating USD 1.7 million in revenue. The region’s leadership is supported by its advanced healthcare infrastructure, strong presence of biotechnology and pharmaceutical companies, and continuous investment in artificial intelligence research.

The United States benefits from major government-backed initiatives such as the National Institutes of Health (NIH) Bridge2AI program, which promotes the development of AI-ready biomedical datasets, ethical AI practices, and collaborative research to accelerate precision medicine and genomics innovation. These efforts strengthen the integration of AI into bioinformatics and life sciences research.

Meanwhile, the Asia-Pacific region is projected to register the fastest growth during the forecast period. Expanding government investments in artificial intelligence, rapid growth of genomics research, improving healthcare infrastructure, and increasing digital health initiatives across countries such as China, Japan, South Korea, and India are expected to accelerate the adoption of AI-driven bioinformatics solutions.

Emerging Trends

• Large-scale multiomics datasets are becoming the foundation for AI bioinformatics. In 2026, the NIH All of Us program released data from 747,000+ participants, including 535,000+ whole genome sequences linked with 482,000 electronic health records, enabling more powerful AI-driven precision medicine research.
• Multiomics integration is expanding beyond genomics. The latest NIH All of Us release added nearly 10,000 proteomics datasets, nearly 9,000 RNA-sequencing datasets, and more than 14,500 long-read whole genomes, giving AI models richer biological information for disease research.
• AI-powered bioinformatics research is accelerating scientific output. The NIH reports that the All of Us research platform has already supported more than 1,400 peer-reviewed publications involving nearly 23,000 researchers, highlighting growing adoption of AI-enabled genomic analysis.
• Biological databases are expanding rapidly to improve AI model training. According to the U.S. Congressional Research Service, researchers have identified more than 1,700 public biological databases containing genomic, protein, metabolic, and structural information for bioinformatics applications.
• Public genomic sequencing capacity continues to grow. The Broad Institute now generates approximately 500 terabases of genomic data every month, providing massive datasets that support AI model development for genomics, disease prediction, and molecular biology.

Use Cases

• AI is improving protein sequence analysis at scale. A 2025 PubMed review identified 63 distinct AI-driven protein sequence analysis tasks, supported by 68 protein databases and 627 benchmark datasets, demonstrating AI’s growing role in protein bioinformatics.
• AI language models are entering protein engineering. The same review found that researchers are using 25 different biological word-embedding methods and 13 protein language models to improve protein function prediction and sequence analysis.
• AI is delivering highly accurate cancer biomarker analysis. A 2025 review published in Briefings in Bioinformatics reported AI models achieving approximately 0.93 AUC for cancer detection using bioinformatics and multiomics datasets, supporting earlier disease identification.
• AI is advancing protein engineering efficiency. The same review reported that AI-based protein design systems have achieved success rates of up to 92% in generating functional protein designs, accelerating therapeutic and biotechnology research.
• Single-cell bioinformatics is benefiting from AI. Recent bioinformatics research reported AI models achieving an AvgBIO score of approximately 0.82 for single-cell biological data analysis, improving interpretation of complex cellular datasets for precision medicine.

Frequently Asked Questions About AI In Bioinformatics

• How does AI improve bioinformatics research?
  AI automates the analysis of complex biological datasets, reducing the time required to process genomic and molecular information. It improves prediction accuracy, supports biomarker discovery, identifies disease-associated genes, and helps scientists make faster, data-driven research decisions.
• What are the major applications of AI in bioinformatics?
  AI is widely used for genome sequencing analysis, protein structure prediction, drug discovery, molecular diagnostics, biomarker identification, precision medicine, and clinical research. It also supports large-scale analysis of omics data, enabling researchers to better understand complex diseases.
• What factors are driving the AI in bioinformatics market?
  The market is expanding because of increasing genomic data generation, growing adoption of precision medicine, advancements in cloud computing, and higher investments in AI-based biomedical research. Government-supported AI initiatives and improved computing capabilities further strengthen market growth.
• Which industries are the primary users of AI in bioinformatics?
  Pharmaceutical companies, biotechnology firms, academic research institutes, healthcare providers, and contract research organizations are the main users. These organizations apply AI to accelerate research, optimize clinical studies, improve diagnostics, and develop new therapeutic solutions.
• Why is machine learning important in bioinformatics?
  Machine learning enables computers to recognize complex biological patterns without explicit programming. It helps classify genetic variants, predict protein functions, analyze sequencing data, and generate accurate biological insights from very large datasets that are difficult to evaluate manually.
• What challenges does the AI in bioinformatics market face?
  Key challenges include maintaining data quality, protecting patient privacy, integrating diverse biological datasets, ensuring AI model transparency, and meeting regulatory requirements. Organizations must also address ethical concerns while developing reliable and reproducible AI-based bioinformatics solutions.
• What is the future outlook for the AI in bioinformatics market?
  The future outlook remains positive as AI becomes increasingly integrated into genomics, molecular biology, and biomedical research. Continued investments in AI-ready datasets, advanced computing infrastructure, and collaborative research programs are expected to expand innovation and commercial adoption.

Conclusion

The global AI in bioinformatics market is poised for remarkable expansion as artificial intelligence becomes an essential component of modern biological and genomic research. Growing investments in AI-ready datasets, precision medicine, multiomics research, and advanced computational technologies are accelerating the adoption of intelligent bioinformatics solutions across healthcare and life sciences.

Supportive initiatives from organizations such as the NIH and FDA, along with continuous advances in machine learning and protein analysis, are strengthening innovation while promoting responsible AI use. Although challenges related to data quality, interoperability, and regulatory compliance remain, ongoing technological progress and collaborative research efforts are expected to drive sustained market growth, improve research efficiency, and enable faster scientific discoveries in the coming years.