AI Innovations in the Biotech Industry: A New Paradigm in Drug Discovery and Development by 2026
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In early 2026, the biotech industry is entering a full-scale commercialization phase of artificial intelligence, experiencing unprecedented changes. According to the latest report by McKinsey & Company, the AI-based drug discovery market is projected to grow from $4.9 billion in 2025 to $24.8 billion by 2030, with an average annual growth rate of 38.2%, which is five times faster than traditional biopharmaceutical R&D investments. Notably, drug development using AI is evolving beyond a mere auxiliary tool to become a core decision-making platform. Leading companies like DeepMind in Cambridge, UK, with AlphaFold3, and Recursion Pharmaceuticals in Boston, USA, are demonstrating verifiable results in clinical trials, rapidly reshaping investment priorities and partnership strategies in the biopharmaceutical industry.
Examining the current status of AI adoption among global biopharmaceutical companies, Roche in Basel, Switzerland, announced that from the second half of 2025, it reduced the time to discover oncology drug candidates from 18 months to 6 months through AI-based drug screening. Pfizer in New York, USA, also succeeded in developing a variant-adapted version of the COVID-19 treatment Paxlovid using its AI platform ‘Digital Sciences,’ achieving a development speed 70% faster than traditional methods. In Korea, Samsung Biologics introduced an AI-based biopharmaceutical production optimization system by the end of 2025, improving production yield by an average of 15%, which is analyzed to bring an additional annual revenue effect of approximately 320 billion won.
Technological Innovations and Achievements in AI-Based Drug Discovery
The area where AI is having the most significant impact in the biotech field is undoubtedly the drug discovery stage. The traditional drug development process, which took an average of 3-5 years from initial candidate discovery to preclinical stages, is being shortened to 12-18 months with the introduction of AI. Atomwise’s AI platform in San Francisco, USA, screened 6.7 million compounds in 2025, discovering 124 promising drug candidates, 23 of which have entered the preclinical trial stage. This shows a success rate 10 times higher than the existing high-throughput screening (HTS) method.
Particularly, the use of AI in protein structure prediction is acting as a game-changer in the biopharmaceutical industry. As of 2025, DeepMind’s AlphaFold3 can predict over 200 million protein structures with 99.5% accuracy, and structure-based drug design utilizing this is becoming mainstream. Moderna in Massachusetts, USA, announced that it is developing a next-generation mRNA vaccine platform using AlphaFold data, achieving 40% improved stability and 25% increased immunogenicity compared to existing platforms. These technological advances are directly influencing the R&D investment strategies of biopharmaceutical companies, with mergers and acquisitions and strategic alliances for securing AI capabilities surging.
Meanwhile, the field of AI-based drug repurposing is also showing noteworthy achievements. BenevolentAI in London, UK, discovered 47 new therapeutic possibilities in 2025 alone through an AI model that identifies new indications for existing approved drugs. Among these, the repurposing of an existing rheumatoid arthritis treatment for ALS (Amyotrophic Lateral Sclerosis) is showing significant effects in Phase II clinical trials, drawing industry attention. Drug repurposing allows the use of existing safety data, reducing development costs by over 80% and shortening the time to market from 5-7 years to 2-3 years, playing a key role in the portfolio diversification strategies of biopharmaceutical companies.
Global Biopharmaceutical Companies’ AI Investment Competition
Global biopharmaceutical companies’ AI-related investments recorded $18.7 billion in 2025, a 156% increase from the previous year, accounting for 23% of total R&D investments. Johnson & Johnson in New Jersey, USA, announced in September 2025 the establishment of an AI-specialized subsidiary ‘J&J Innovation Labs,’ with plans to invest $4.5 billion over the next three years. This massive investment, accounting for 30% of the company’s annual R&D budget, aims for digital transformation across the entire value chain, from AI-based drug discovery to clinical trial optimization and manufacturing process innovation. Notably, J&J secured 12 new pipelines in the immuno-oncology field through its self-developed AI platform, with 3 expected to enter clinical trials in the first half of 2026.
Novartis in Basel, Switzerland, took a different approach. In early 2025, it entered into a $1.5 billion long-term partnership with AI startup Isomorphic Labs, opting to actively utilize external expertise rather than internalizing AI-based drug design capabilities. Through this collaboration, Novartis is focusing on developing treatments for rare diseases, with 7 AI-designed drugs currently in the preclinical stage. On the other hand, AbbVie in Chicago, USA, focused on strengthening internal AI capabilities, increasing its AI specialist workforce by 340% in 2025, and accelerating new drug development in the field of immunology through its self-developed ‘Compass AI’ platform.
In the Asian market, Korean and Chinese companies are showing unique approaches. Korea’s Celltrion introduced AI in biosimilar development through a strategic alliance with domestic AI startup Standigm in the second half of 2025. This reduced the development period for biosimilars from 5 years to 3 years and achieved a 40% reduction in development costs. In China, InventisBio in Shanghai is making a mark in the field of antibody optimization with its self-developed AI platform, having signed collaboration agreements with 17 global pharmaceutical companies as of 2025. These region-specific AI capabilities are accelerating the multipolarization of the global biopharmaceutical ecosystem, leading to the development of differentiated AI solutions tailored to the regulatory environment and market characteristics of each region.
From an investment perspective, venture investments in the AI biotech sector recorded $8.9 billion in 2025, a 67% increase from the previous year. Particularly, Series A stage investments accounted for 42% of the total, indicating growing interest in early-stage companies. A representative success story is Generate Biomedicines in Boston, USA, which raised $730 million in a Series C round in April 2025. The company is gaining attention for its protein design technology using generative AI, with 3 pipelines currently preparing for IND (Investigational New Drug) approval. Such large-scale investment attraction is interpreted as an important indicator of the technology validation and commercialization potential of AI biotech companies.
However, challenges related to AI adoption are not insignificant. The biggest issue is data quality and standardization. In the case of biomedical data, collection methods and standards vary by institution and country, limiting the generalization performance of AI models. In December 2025, the US FDA released the ‘AI in Drug Development Guidance,’ presenting a regulatory framework for AI-based drug development, but uncertainties still exist in clinical trial design and approval processes. Additionally, the explainability of AI models remains a major concern for regulatory authorities and medical professionals. These challenges significantly influence the technology development direction and regulatory response strategies of AI biotech companies and are expected to act as key variables determining the pace of market growth in the future.
As of 2026, AI innovations in the biotech industry are driving a paradigm shift across the entire industrial ecosystem beyond mere technological advancement. Traditional pharmaceutical companies are engaging in large-scale investments and mergers and acquisitions to secure AI capabilities, while AI biotech startups are accelerating technology validation and commercialization through partnerships with global pharmaceutical companies. These changes are expected to fundamentally reshape the competitive landscape of the biopharmaceutical industry over the next 5-10 years, with companies successfully internalizing AI technology likely to secure leadership in the next-generation biopharmaceutical market. Particularly, with the advancement of personalized medicine and precision medicine, the AI biotech market is expected to sustain high growth of over 35% annually until 2030, which is analyzed to have a positive impact on the stock prices and investment returns of related companies.
This content is for informational purposes only and is not intended as investment solicitation or advice. Investment decisions should be made at the individual’s discretion and responsibility, and thorough research and consultation with experts are recommended when investing in the mentioned companies or stocks.
