AI Integration in Biotechnology Industry Reaches a New Turning Point in 2025

In 2025, the biotechnology industry is reaching a new turning point through the full-fledged integration with artificial intelligence. The global bio-AI market is projected to grow by 33%, from $3.9 billion in 2024 to $5.2 billion in 2025, and is expected to reach $28.5 billion by 2030 with an average annual growth rate of 28.4%. This rapid growth is driven by the accelerated digital healthcare transition post-COVID-19 pandemic, increased demand for personalized medicine, and above all, the remarkable advancement of AI technology.

One of the notable trends in 2025 is the commercial success of ‘AI-based drug development platforms.’ The UK-based Exscientia announced that its first AI-developed drug candidate showed positive results in Phase 2 clinical trials, reducing the development period by 40% compared to traditional processes. Similarly, U.S. company Recursion Pharmaceuticals has five drug candidates discovered through its AI platform currently undergoing clinical trials, with two expected to announce significant results in the second half of 2025. These achievements demonstrate to biotech investors that AI-based drug development is no longer a future technology but a current business reality.

The Korean biotech industry is actively responding to this global trend. Samsung Biologics, headquartered in Seongnam, Gyeonggi Province, announced a 120 billion KRW investment in March 2025 to establish an AI-based biopharmaceutical development platform. This strategy is linked to Samsung Group’s AI strategy, combining Samsung SDS’s AI capabilities with Samsung Biologics’ bio-manufacturing expertise to build a next-generation biopharmaceutical development ecosystem. Additionally, Celltrion, based in Incheon, began operating its AI-based antibody design platform in the fourth quarter of 2024, with its first antibody candidate expected to enter preclinical trials in the first half of 2025.

Technological Innovations and Market Dynamics in AI Drug Development

The core of AI-based drug development technology is learning vast biological data to predict and design new drug candidates. Notably, advancements in protein structure prediction technology are bringing innovation to this field. DeepMind’s AlphaFold, which has released a database of over 200 million protein structures, is available for free to researchers worldwide, rapidly advancing drug target discovery. As of 2025, there are approximately 3,400 drug development projects worldwide utilizing AlphaFold data, with about 15% entering the clinical trial phase.

Genentech, a Roche subsidiary based in San Francisco, California, announced in February 2025 that it discovered a new anticancer drug candidate through its AI-based antibody-drug conjugate (ADC) development platform. This platform uses machine learning algorithms to optimize antibody binding sites and predict drug delivery efficiency, showing a 30% improvement in therapeutic effects compared to existing methods. Roche plans to build more than 10 ADC pipelines over the next three years based on this technology.

In contrast, Johnson & Johnson, headquartered in New Jersey, is taking a somewhat conservative approach. The company prioritizes using AI technology for drug repurposing and clinical trial optimization rather than new drug discovery. The head of J&J’s innovation division stated, “AI technology provides clear value in drug development, but a phased approach is needed in terms of regulatory environment and safety verification.” This difference in approach highlights the varying views on AI adoption strategies among major pharmaceutical companies.

In the AI drug development field, the use of ‘generative AI’ is particularly noteworthy. Beyond simply analyzing existing data, technology that generates entirely new molecular structures is being commercialized. Mila AI, based in Cambridge, UK, announced that a new antibiotic candidate designed with generative AI showed a 99.7% inhibitory effect against multidrug-resistant bacteria. This is more than ten times the improvement compared to existing antibiotics, offering a new breakthrough in addressing antibiotic resistance issues.

Digital Transformation and Competitive Dynamics in Biomanufacturing

AI technology is driving innovation not only in drug development but also in biomanufacturing processes. AI adoption is accelerating at every stage of biopharmaceutical production, including cell line development, culture condition optimization, and quality control. Samsung Biologics announced the full implementation of an AI-based smart manufacturing system at its fourth plant in Songdo, Incheon, in 2025. This system analyzes real-time process data to automatically adjust optimal production conditions, expected to improve yield by 15-20% compared to existing methods.

The status of Korean companies in the global biomanufacturing market is also rising significantly. Samsung Biologics recorded sales of 2.8 trillion KRW in 2024, a 24% increase from the previous year, and aims to surpass 3.3 trillion KRW in 2025. The productivity improvement from AI technology adoption is a major driver of sales growth. Celltrion also achieved a record high in biosimilar sales, reaching 1.95 trillion KRW in 2024, and plans to secure new growth drivers by expanding its AI-based drug pipeline in 2025.

Moderna, headquartered in Cambridge, Massachusetts, is developing next-generation vaccines and therapeutics through the convergence of mRNA technology and AI. The company announced that its RSV vaccine, developed through an AI-based mRNA sequence design platform, induced more than twice the immune response compared to existing vaccines in Phase 2 clinical trials. Based on these results, Moderna plans to add more than 20 new mRNA therapeutic candidates to its pipeline in the second half of 2025. Particularly in the field of personalized cancer vaccines, the accuracy of tumor antigen prediction using AI technology reaches 85%, increasing the potential for commercialization.

Another key aspect of AI adoption in biomanufacturing is supply chain optimization. Efforts to address the vulnerabilities of the global biopharmaceutical supply chain exposed by the COVID-19 pandemic through AI technology are in full swing. Roche, headquartered in Basel, Switzerland, announced the implementation of an AI-based demand forecasting system at its global production facilities in 2025. This system comprehensively analyzes disease incidence rates, prescription patterns, and inventory levels in each country to establish optimal production plans, expected to reduce inventory costs by 30% while significantly enhancing supply stability.

These changes are also altering the criteria for assessing the competitiveness of biomanufacturers. In the past, simple production scale and cost competitiveness were the main factors, but now AI technology capabilities, data analysis skills, and digital infrastructure levels are emerging as new differentiators. Particularly, Asian biomanufacturers are actively investing in this digital transformation, bringing new changes to the traditionally Western-centric biomanufacturing ecosystem.

From an investment perspective, venture investment in the bio-AI sector in 2025 is expected to increase by 45% from the previous year, reaching $12.7 billion. Notably, late-stage investments after Series B account for 62% of the total, indicating that AI biotech companies are moving beyond the proof-of-concept (PoC) stage and approaching commercialization. In Korea, support for AI biotech startups is expanding through the government’s K-Bio Grand Challenge program, with a total investment of 80 billion KRW planned for 2025.

However, despite these growth prospects, the bio-AI industry faces significant challenges. The biggest issue is regulatory uncertainty. The U.S. FDA released a draft guideline for AI-based drug development at the end of 2024, but specific approval criteria remain unclear. The European Medicines Agency (EMA) is in a similar situation, requiring consensus between the industry and regulatory authorities on the methodology for evaluating the safety and efficacy of AI-developed drugs. The Korean Ministry of Food and Drug Safety also plans to release AI drug development guidelines in the first half of 2025, but harmonizing with global standards remains a critical task.

Data quality and accessibility issues also remain challenges to be addressed. The performance of AI models critically depends on the quality and quantity of training data, yet high-quality data in the bio field is still scarce and access is limited. Particularly, the lack of data on rare diseases or specific ethnic groups can lead to bias in AI models, making the construction of inclusive and diverse datasets an urgent task. To address this, global efforts to build bio data-sharing platforms are actively underway, with Korea participating through its National Bio Big Data project.

In conclusion, the AI integration in the biotechnology industry in 2025 has moved beyond the experimental stage and entered a full-fledged commercialization trajectory. Concrete achievements are emerging in various areas, including innovation in drug development processes, improved biomanufacturing efficiency, and the realization of personalized medicine. Korean biotech companies are accelerating AI technology adoption in line with global trends, establishing new competitive advantages through the combination of manufacturing capabilities and AI technology. However, resolving challenges such as regulatory clarity, data accessibility improvement, and workforce development is essential for these innovations to lead to sustainable growth. For investors, it is crucial to select companies with both technological capabilities and regulatory responsiveness, with particular attention to the global competitiveness enhancement of Korean biotech companies.

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**Disclaimer**: This analysis is based on publicly available information and should not be interpreted as investment advice or a trading signal. All investment decisions should be made at one’s own discretion and responsibility, with full consideration of the potential risks of loss.