Antibody Discovery Market Expected to reach USD 7.18 Billion by 2032

The antibody discovery market, projected to be valued at USD 3.15 Billion in 2025, is set for significant growth, with expectations to reach USD 7.18 Billion by 2032, registering a robust CAGR of 12.5% over the forecast period. This expansion is primarily driven by the rising demand for targeted therapies across oncology, immunology, and infectious diseases, alongside the increasing adoption of monoclonal antibodies in drug development.

Technological advancements in high-throughput screening, single-cell analysis, and next-generation sequencing are accelerating antibody identification and optimization. Furthermore, growing biopharmaceutical R&D investments and the emergence of AI-driven discovery platforms are enhancing the speed, precision, and cost-efficiency of antibody development, collectively fueling the market’s expansion across both established and emerging therapeutic areas.

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The antibody discovery market is experiencing accelerated growth, fueled by the increasing demand for targeted biologics in the treatment of cancer, autoimmune disorders, and infectious diseases. As the biopharmaceutical industry shifts toward precision medicine, there is a growing need for high-affinity, specific antibodies capable of modulating disease pathways with minimal off-target effects. This demand is driving advancements in discovery platforms such as phage display, B-cell receptor sequencing, and single-cell screening technologies, which are enabling faster and more precise identification of therapeutic antibody candidates.

Antigen design continues to play a pivotal role in therapeutic innovation by allowing researchers to model and create novel target epitopes, thereby improving antibody specificity and reducing immunogenicity. Hybridoma-based methods, though traditional, remain foundational due to their proven efficacy in producing stable monoclonal antibodies and are widely used in both academic and industrial research.

In July 2024, AbCellera and Eli Lilly announced a major expansion of their collaboration to discover therapeutic antibodies in immunology, cardiovascular, and neuroscience. AbCellera’s AI-powered antibody discovery platform was central to the deal, reinforcing the growing role of machine learning in therapeutic antibody development.

Additionally, regulatory agencies such as the U.S. FDA and EMA are also supporting the growth of this market by streamlining approvals for novel biologics through expedited pathways such as Fast Track and Breakthrough Therapy designations. This, combined with rising investments in biologics R&D, favorable reimbursement environments, and the integration of digital tools for antibody screening and optimization, is positioning the antibody discovery market for sustained expansion and innovation through 2032.

The antibody discovery market is witnessing a rapid integration of artificial intelligence (AI) and machine learning (ML) across various stages of development, from antigen prediction and epitope mapping to CDR sequence optimization and developability assessments. These technologies are enabling faster, more accurate identification of high-affinity antibodies while minimizing immunogenicity and off-target effects.

In January 2023, Berkeley Lights launched the Beacon Select optofluidic system for cell line development. This single-cell platform enables simultaneous cloning, culture, and functional screening of thousands of B cells, streamlining the discovery of antibody-secreting clones.

Antibody discovery is being transformed by next-generation display libraries engineered for greater diversity, stability, and drug-like characteristics. These platforms are enabling the selection of rare, high-affinity clones against complex or previously undruggable targets.

In January 2024, researchers introduced a semi-synthetic Fab library combining Fab phage display with scFab yeast display, yielding highly developable, sub-nanomolar binders that convert efficiently to full IgG formats, showcasing a streamlined approach to generate drug-like candidates directly.

Countries such as India, China, Brazil, and Southeast Asia are rapidly investing in biotechnology infrastructure, academic research, and translational medicine initiatives. Despite growing demand, many of these regions still face challenges in accessing cutting-edge antibody discovery platforms due to high costs, limited training, and restricted availability of high-throughput screening tools. This presents a significant opportunity for antibody discovery service providers and reagent manufacturers to expand through regional partnerships, localization of services, and tailored pricing strategies.

In June 2025, the Translational Health Science and Technology Institute (THSTI) in Faridabad hosted a high-level symposium that brought together industry leaders, academia, startups, and funding agencies like BIRAC. The event highlighted ongoing efforts to establish India’s first “bio-clusters” and emphasized the government’s new Bio-E3 policy to promote indigenous monoclonal antibody (mAb) discovery, manufacturing, and industry-academia collaboration.

As the complexity of antibody engineering increases, there is rising demand for cloud-native platforms that integrate in silico antibody design, developability prediction, and ML-guided optimization. Cloud-based discovery tools enable real-time collaboration, high-throughput data processing, and secure, scalable workflows across geographies. This creates significant growth opportunities for SaaS providers and AI-driven discovery platforms to partner with pharma and biotech firms globally.

In May 2025, Viva Biotech launched its comprehensive AI-Driven Drug Discovery (AIDD) platform, comprising three core modules—V-Scepter, V-Orb, and V-Mantle. This platform integrates artificial intelligence with Viva's extensive experience in structure-based drug discovery, aiming to dramatically reduce drug development timelines and costs while improving success rates. Notably, the AI-driven antibody discovery workflow achieved a 400% increase in efficiency with an 85% success rate in candidate nomination.