A New Turning Point in Biotech Innovation: AI-Driven Drug Development Reshaping the Pharmaceutical Industry by 2025
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Structural Changes in the Biotech Industry Triggered by the AI Revolution
As of the end of 2025, the biotechnology industry is undergoing the most radical transformation in its history due to the full-scale adoption of artificial intelligence technology. According to McKinsey Global Institute, the global AI-based drug development market grew by 37%, from $3.5 billion in 2024 to $4.8 billion in 2025, and is expected to reach $28 billion by 2030 with an average annual growth rate of 32%. This rapid growth is redefining not just the adoption of technology but also fundamentally altering the business models and value creation methods across the pharmaceutical industry.
Notably, significant changes are occurring in the early stages of drug development, specifically in target discovery and lead compound optimization. Traditionally, these stages alone required 3-5 years and billions of dollars, but companies utilizing AI-based platforms are reducing this process to 18-24 months and cutting costs by 60-70%. The UK-based Exscientia announced that its AI-designed obsessive-compulsive disorder treatment showed 40% higher efficacy in Phase 2 clinical trials compared to existing treatments, demonstrating the tangible outcomes of AI-driven drug development.
Korea’s biotech ecosystem is also rapidly evolving in line with these global trends. Samsung Biologics announced an investment of 250 billion won in an AI-based biopharmaceutical development platform by the third quarter of 2025, aiming to reduce development time by 50% by 2027. Celltrion has also established its own AI research lab and begun applying machine learning technology to antibody drug development. These investments are interpreted as strategic choices to secure global competitiveness rather than mere technology adoption.
Global pharmaceutical giants are taking even more proactive steps. Johnson & Johnson, headquartered in New Jersey, USA, invested $1.5 billion in AI drug development in 2025, an 80% increase from the previous year. Pfizer, headquartered in New York, is accelerating Alzheimer’s drug development using the Watson for Drug Discovery platform through a partnership with IBM, reporting that initial results yielded candidate compounds three times faster than traditional methods. Roche, headquartered in Basel, Switzerland, is focusing on AI-based personalized cancer treatment development through its Genentech subsidiary, investing a total of $400 million in three startups in the field in the second half of 2025 alone.
The New Therapeutic Paradigm Created by the Convergence of mRNA Technology and AI
mRNA technology, which gained global attention during the COVID-19 pandemic, is sparking another wave of innovation in the biotech industry as it combines with AI. Moderna, headquartered in Cambridge, Massachusetts, announced that its pipeline developed through an AI-based mRNA design platform reached 48 projects in 2025, a 60% increase from 2024. In particular, the accuracy of neoantigen prediction using AI in the personalized cancer vaccine field reached 85%, showing significant improvement over traditional methodologies.
BioNTech, headquartered in Mainz, Germany, is utilizing AI technology with a different approach than Moderna. They have established an integrated development system that simultaneously optimizes mRNA sequences and lipid nanoparticles (LNP) design through their proprietary AI platform in 2025. The next-generation COVID-19 vaccine developed through this system showed a 45% improvement in neutralizing antibody generation and a 30% reduction in side effects compared to existing vaccines, as confirmed by clinical data. BioNTech’s stock price rose 78% from the beginning of 2025, driven by these technological achievements.
The application of mRNA technology is expanding beyond vaccines to therapeutic areas. mRNA-based protein replacement therapy for rare diseases is a representative case. This approach, which directly produces deficient enzymes or proteins in the body through AI-designed mRNA sequences, offers superior convenience and efficacy compared to traditional protein injection therapies. According to market research firm Grand View Research, the mRNA therapeutics market is expected to grow explosively from $1.2 billion in 2025 to $7.8 billion by 2030, with an average annual growth rate of 45%.
Domestic companies are also actively pursuing mRNA technology development. SK Bioscience unveiled its proprietary mRNA platform technology in the second half of 2025, announcing that its RSV vaccine demonstrated safety in Phase 1 clinical trials. Additionally, LG Chem, through its subsidiary LG Life Sciences, has secured mRNA API production technology and is pursuing partnerships with global mRNA vaccine developers. These moves are evaluated as strategic attempts for Korea to move beyond being a latecomer in mRNA technology and secure global competitiveness.
However, the opportunities brought by the convergence of mRNA technology and AI are accompanied by significant challenges. The biggest issue is establishing manufacturing infrastructure for mass production. mRNA is less stable than DNA, and the manufacturing process is complex, presenting high technical barriers to large-scale production. Currently, global mRNA production capacity is only about 40% of annual demand, leading to continuous increases in raw material prices. According to Moderna’s third-quarter 2025 earnings report, mRNA raw material procurement costs increased by 25% compared to the same period last year, impacting the company’s operating profit margin by 3.2 percentage points.
Changes in Regulatory Environment and the Restructuring of the Global Biotech Ecosystem
The rapid advancement of AI-based biotechnology presents new challenges to regulatory authorities worldwide. The US FDA issued the ‘AI in Drug Development Guidance’ in May 2025, clarifying the regulatory framework for AI-based drug development. According to this guidance, the transparency and verifiability of AI algorithms have become key elements for new drug approval, requiring developers to document the training data and decision-making processes of AI models in detail. Although these regulatory changes may increase development costs for biotech companies in the short term, they are expected to have a positive effect in the long term by enhancing trust in the safety and efficacy of AI-based drugs.
The European Medicines Agency (EMA) is taking a more comprehensive approach. The ‘Digital Health Technologies in Medicine Regulation’ announced in September 2025 presents a regulatory framework encompassing not only AI but also digital therapeutics and remote monitoring in digital healthcare. Notably, it established an approval pathway for continuously learning AI systems utilizing real-world data. This is considered innovative as it opens the way for AI models to be continuously improved through data generated during actual patient treatment processes, rather than static clinical trial data.
In Asia, countries are adopting different regulatory approaches, showing interesting contrasts. Japan expanded the ‘Sakigake Designation System’ to AI-based biotech in April 2025, offering up to 50% reduction in review periods and priority review benefits for innovative AI drugs. As a result, AI investments by Japanese biotech companies surged, with investments in the field increasing by 120% year-on-year in the first half of 2025. In contrast, China is taking a more cautious approach. The National Medical Products Administration (NMPA) is applying stricter clinical trial requirements for AI-based drugs, resulting in a relatively slower pace of AI adoption among Chinese biotech companies.
Korea’s Ministry of Food and Drug Safety (KFDA) announced the ‘AI-based Drug Development Guidelines’ in July 2025, advancing regulatory modernization. The core of these guidelines is the ‘Regulatory Sandbox’ system, which applies limited but flexible regulations to innovative AI biotech technologies. Companies selected for the sandbox can be exempted from some existing regulations and conduct demonstration projects, with a pathway to formal approval based on the results. As of the second half of 2025, 12 companies are benefiting from this system, with three already receiving clinical trial approvals.
Changes in the regulatory environment are reshaping the landscape of the global biotech ecosystem. Research and development bases of biotech companies are shifting to countries with relatively flexible regulations, directly impacting the biotech competitiveness of each nation. Singapore is actively leveraging this opportunity to emerge as an Asian biotech hub. Through the ‘BioTech Innovation Hub’ program in 2025, the Singapore government is offering up to 10 years of corporate tax exemption and a 200% R&D tax deduction to AI-based biotech companies, with over 20 global biotech firms already relocating their Asian headquarters to Singapore.
From an investment perspective, the clarification of the regulatory environment is significantly increasing institutional investors’ interest in the biotech sector. The global biotech venture investment volume in 2025 increased by 45% year-on-year to $28 billion, with 60% concentrated in AI-based companies. Notably, sovereign funds, traditionally passive in biotech investments, are making large-scale investments. Saudi Arabia’s Public Investment Fund (PIF) invested $1.5 billion in the AI biotech sector in the second half of 2025 alone, and Norway’s sovereign wealth fund tripled its related portfolio compared to the previous year. This influx of large-scale capital is dramatically enhancing the R&D capabilities of biotech companies while driving up valuations across the industry.
However, despite these rapid changes, challenges remain to be addressed. The biggest issue is the gap between the actual clinical outcomes of AI-based biotech and market expectations. To date, only 12% of drugs developed with AI have successfully completed Phase 3 clinical trials, a lower rate than the traditional drug development success rate of 15%. This reality highlights both the limitations of AI technology and the inherent uncertainties of the biotech field, requiring cautious approaches from investors and regulatory authorities alike. For the sustainable growth of the biotech industry, it is deemed necessary to manage realistic expectations alongside technological innovation and maintain patience from a long-term perspective.
*This content is not intended as investment advice or stock recommendations, and the final decision on investments rests with the investor’s own judgment and responsibility.*
