Frost & Sullivan officially releases the '2024 AI Life Sciences Market Research Report' | AI life sciences drive systematic upgrade of transformational medicine's full-process solutions

AI life omics combines artificial intelligence with life sciences, analyzing multi-dimensional data such as genomes, transcriptomes, and proteomes through AI algorithms to reveal the complex mechanisms of life activities. These data come from clinical cohorts, and AI technology improves the accuracy of data analysis through methods such as data dimensionality reduction and denoising. AI life omics has promoted precision diagnosis, drug development, and personalized medicine.

Its main features include efficient data management and integrated analysis of multi-omics data. AI can bridge the barriers between different omics disciplines, reveal interactions between genes, proteins, and metabolites, and provide support for disease mechanism research and targeted therapy. In drug development, AI helps identify new drug targets, optimize drug performance, and accelerate the research process. In personalized medicine, AI provides customized treatment plans by analyzing patients' omics data.

The AI life omics market covers multiple areas, including AI cohort data center solutions. By intelligently managing patient data and clinical research information, it improves the efficiency of cohort studies and supports precision medicine and new drug development. The AI-BT software platform uses AI algorithms to parse life omics data, helping researchers manage and interpret data efficiently. Multigroup omics analysis and AI model development promote the revelation of disease molecular mechanisms and the discovery of drug targets, providing tools for clinical decision-making. Medical engineering translation and drug R&D support improve drug success rates by optimizing drug performance and shortening the R&D cycle. AI medical technology services provide data collection, management, and analysis support for disease diagnosis and personalized treatment.

The AI life omics market has gone through four development stages: the initial phase of genomics (2000-2010), where AI was mainly used for gene sequence alignment and functional annotation; the expansion phase of proteomics (2010-2020), when AI made breakthroughs in protein structure prediction and functional analysis; the multi-omics integration phase (2020-2023), when AI was widely applied to multi-omics data analysis, promoting cancer typing and drug target discovery; and the growth phase (since 2023), where AI has deepened its application in data integration and clinical cohort management, driving precision medicine and new drug research and development.

The development of AI life omics technology has evolved from genomics to metagenomics, encompassing the intersection and integration of multiple disciplines. Initially, genomics laid the foundation with the discovery of the DNA double helix structure and the completion of the Human Genome Project. Subsequently, technologies such as epigenomics, transcriptomics, and proteomics have continuously introduced new advancements, driving in-depth biological research. The development of metabolomics, lipidomics, and microbiomics has further expanded the scope of life omics research. The application of large-scale models for DNA and protein sequences, coupled with deep learning technology, has significantly improved the accuracy of gene identification and functional prediction.

In addition, AI life omics technology involves a complete technical chain, including the establishment of biological sample libraries, queue research, multi-omics detection, data preprocessing, and bioinformatics analysis. The widespread application of high-throughput sequencing technology provides strong support for precision medicine. Combined with AI algorithms, it can deeply analyze cell data, gene variations, and functions, promoting the progress of biological research and clinical applications.

AI life omics technology has promoted the systematic development of life science research through data integration and intelligent analysis, providing a new technical path for disease prevention, treatment, and precision medicine.

AI life omics is driving a profound transformation in life science research, integrating and deeply analyzing multi-level biomedical data to achieve efficient analysis from the cellular level to the overall patient level. Its data sources include genomics, transcriptomics, epigenomics, metabolomics, etc., which not only cover the molecular level but also include structural features of tissues and phenotypic information of patients. However, facing such vast and diverse data, traditional analysis methods seem inadequate and difficult to fully tap into the potential within the data. Therefore, the application of AI technology has become an effective solution, capable of integrating data from different modalities at the molecular level and improving analysis efficiency. AI can combine RNA gene expression with spatial localization to draw cell distribution maps, and can also deeply explore the functional characteristics of complex tissues, helping to achieve breakthroughs in areas such as pathogenesis research, new drug target discovery, tissue regeneration and repair, as well as early disease screening and diagnosis.

AI life omics has demonstrated tremendous potential in multiple fields. In the study of pathogenesis, combining AI with multi-omics data can identify cellular subpopulations and microenvironmental characteristics at the onset of diseases, thereby providing guidance for personalized immunotherapy. In drug development, the use of 3D molecular tissue models has accelerated the discovery of potential targets and shortened the R&D cycle. In the field of regenerative medicine, AI-assisted analysis of metabolomics data has promoted research on molecular mechanisms of tissue repair and regeneration. AI can also use clinical data for early disease screening and precise diagnosis, improving intervention effectiveness and facilitating the transformation from basic research to clinical application.

The market for AI life omics is also growing rapidly. From 2020 to 2028, the market size increased from 16.4 billion yuan to 70.3 billion yuan, with an average annual compound growth rate of 24.79%. This growth is mainly due to the push on healthcare demand by the COVID-19 pandemic and the widespread application of AI technology in precision medicine and drug research and development. With continuous technological iteration and upgrading, the application scenarios of AI life omics will further expand, involving multiple fields such as precision medicine, public health management, and personalized medicine, driving the development and commercialization process of the industry.

In recent years, the Chinese government has introduced systematic policies to support the development of AI life omics, emphasizing the deep integration of AI with life sciences and promoting the coordinated development of the bioeconomy and digital economy. The policy priorities include accelerating the transformation of basic research findings into clinical applications and improving the efficiency of precision medicine and drug research and development. At the same time, the policy strengthens data governance and standardization construction to ensure the secure and standardized management of health medical data, thereby promoting data sharing and integration in scientific research and applications.

In terms of technological innovation and industrialization, the state provides financial support through key research and development programs, precision medicine projects, etc., to promote collaborative innovation between enterprises and scientific research institutions. The policy has driven a rapid transformation from research to market, establishing a model driven by both technology and market.

The AI life omics industry chain includes various links from data collection and integration to clinical application. The main links include: information collection and integration, sample bank and cohort construction, target screening, drug design synthesis, efficacy prediction, and clinical data analysis. Although the industry chain has been initially established, there are problems such as data silos and low collaboration, which require strengthening data standardization, technology sharing, and cross-border cooperation in the future.

AI life omics faces issues such as chaotic data management, poor system integration, and insufficient depth of intelligent analysis applications. Data silos and the lack of efficient sharing mechanisms limit the depth of application of AI algorithms. At the same time, there is a lag in the application of AI technology, especially in the medical field, where the implementation effect is not satisfactory. The industry also faces challenges such as privacy security, cross-platform collaboration, and talent shortage. In the future, breakthroughs are needed in data management, technology integration, and privacy protection.

The entry barriers for AI life omics mainly include high technical requirements, market integration difficulties, and data acquisition complexity. The development of high-precision algorithms requires interdisciplinary teams and long-term investment, data integration is challenging and subject to regulatory restrictions, especially when it involves patient privacy. Companies need to possess deep technical reserves, industry resources, and compliance capabilities to break through these barriers and promote industrial integration and innovation.

The market demand for AI life omics mainly comes from precision medicine, drug research and development, internationalization of traditional Chinese medicine, and innovation in medical devices. Technological progress and policy support drive market growth; AI technology has enhanced data analysis capabilities, while cloud computing and other technologies have lowered application thresholds. Future opportunities include the vaccine and veterinary drug sectors, where AI can accelerate research and development and optimization to improve vaccine development efficiency and precision.

Caiyun Qiyuan is a domestic biotech company focusing on AI + omics analysis. It has formed an integrated service system of data + algorithms + platforms in fields such as infection, tumors, and genetic diseases, covering a multi-dimensional product matrix ranging from bioinformatics analysis, scientific research data middle platforms to LIMS systems and sample library management. Relying on its full-process data capabilities and AI modeling technology, the company can provide omics modeling, pathogen tracing, AI drug screening, and clinical decision support services for vaccine design, drug development, and other links. It has broad potential in the directions of domestic substitution and AI empowering life sciences.

Data source: Frost & Sullivan analysis, company website

Velsera is a global healthcare data integration platform company formed by the merger of DNAnexus and BC Platforms. It aims to connect multi-omics data with clinical information, empowering precision medicine practices in pharmaceutical companies, research institutions, and hospitals. The platform connects the three ends of biopharmaceuticals, medical research, and clinical treatment. Through technical means such as data governance, knowledge graph construction, and AI analysis tools, it promotes the implementation of high-value-added applications such as new drug development, early model building, and personalized treatment.

UK Biobank is a national-level biological sample and health database platform in the UK, having collected high-dimensional data such as genomes, health records, lifestyles, and imaging from over 500,000 individuals. It serves as a core infrastructure for global life science research and AI medical modeling. Its data open sharing strategy has accelerated global scientific collaboration and is a foundational resource for training sets for multiple international AI medical models (such as DeepMind, OpenTargets, etc.).

InSilicon Intelligence was established in 2014 and headquartered in Hong Kong. It is a global leader in AI-driven new drug research and development, building AI platforms covering target discovery, compound design, and clinical trial prediction, including PandaOmics, Chemistry42, and InClinico. The platforms use deep learning and generative AI to improve the efficiency of new drug research and development and have deployed pipelines in multiple disease areas. The company adopts three business models: "self-developed + collaboration + SaaS services," has cooperated with several international pharmaceutical companies, and has completed over $400 million in financing. Its technical advantages lie in the closed-loop nature of the AI platform, algorithm capabilities, and global cooperation resources, effectively supporting the entire R&D process from target discovery to clinical development and accelerating drug market launch.

Data source: Frost & Sullivan analysis, company website

PureBase was established in 2014 and is a life science company focusing on preclinical CRO services and tumor research, dedicated to creating AI-driven precision medicine solutions. Its main services include patient-derived model (PDX) construction, tumor organoid development, pharmacodynamic experiments, and multi-omics data analysis, helping pharmaceutical companies with target validation and preclinical evaluation. The company's strengths lie in its rich tumor model resources and complete experimental service system, with nearly a thousand PDX models and a large-scale organoid database. PureBase's AI platform can assist in analyzing drug responses and mechanisms, improving research and development decision-making efficiency. It has now served over a hundred pharmaceutical and biotechnology companies and holds a high industry position in the field of precision oncology.

Genedata is a software technology company headquartered in Basel, Switzerland, focusing on providing scalable and highly integrated data solutions for biopharmaceutical research and development. Its flagship product, Biopharma Platform, supports the entire process from molecular screening, data analysis to preclinical research, and has become an important technical support for the digital transformation of many international large pharmaceutical companies. Genedata emphasizes system openness and AI compatibility, giving it a significant advantage in integrating experimental data and automating workflows. Since its establishment in 1997, the company has continuously expanded its capabilities in drug research and development data management and AI model construction, helping the biopharmaceutical industry achieve intelligent upgrading.