The AI Revolution in the Biotechnology Industry: Prospects for Drug Development and Precision Medicine Market in 2025
Editor
By 2025, the biotechnology industry is experiencing an unprecedented transformation through its convergence with artificial intelligence. The global AI-driven drug development market is projected to surge from $8.5 billion in 2024 to $12 billion in 2025, marking a 41% growth, which indicates a fundamental shift from traditional drug development methods. Particularly, drug discovery platforms utilizing machine learning and deep learning technologies are significantly reducing the drug development period from the conventional 10-15 years to 5-7 years, thereby drastically improving R&D investment efficiency in the pharmaceutical industry. In Korea, the government’s announcement of the ‘K-Bio Grand Challenge’ project, which includes a plan to invest 3 trillion won in the AI-based bio sector by 2025, is accelerating the adoption of AI among domestic biotech companies.
Currently, the most active area of AI application in the biotechnology sector is in the discovery of new drug targets and the optimization of compounds. Recursion Pharmaceuticals, based in California, USA, automates over 2 million biological experiments weekly through its AI platform, with 15 of the discovered drug candidates now in clinical trial stages. In the UK, Exscientia’s AI-developed OCD treatment, DSP-1181, demonstrated a 30% improvement in efficacy over existing treatments in Phase 2 clinical trials in 2024, garnering industry attention. These achievements demonstrate that AI is not merely an auxiliary tool but a core engine in drug development.
In Korea’s biotech industry, Samsung Biologics is setting a leading example of AI adoption. Based in Songdo, Incheon, Samsung Biologics began applying AI to optimize biopharmaceutical production processes through its self-developed ‘Bio-AI Platform’ in the second half of 2024. This platform analyzes real-time data across all production stages, including cell culture conditions, purification processes, and quality control, achieving an average yield improvement of 15%. Particularly, in the contract manufacturing (CMO) projects conducted at its fourth plant, AI-based process management has reduced production time by 20%, earning high praise from client companies.
Celltrion is also actively investing in AI-based biosimilar development. Headquartered in Incheon, Celltrion utilized AI algorithms for structural analysis of active pharmaceutical ingredients and optimization of production processes during the development of its bevacizumab biosimilar ‘CT-P16,’ scheduled for release in the first half of 2025. This approach reduced the development period by 18 months compared to traditional methods, and clinical trials confirmed 98.5% equivalence to the original drug. Celltrion currently has a pipeline of 13 biosimilars, with AI technology being applied in the development of 8 of these products.
AI Innovation in Precision Medicine and Personalized Treatment
Another key area of AI application in the biotechnology field is precision medicine and personalized treatment. The global precision medicine market is expected to grow from $105 billion in 2024 to $128 billion in 2025, with the proportion of AI-based solutions expanding from 35% to 45%. AI utilization is rapidly increasing in areas such as genomic analysis, biomarker discovery, and treatment response prediction. Solutions combining Illumina’s next-generation sequencing (NGS) platform with AI analysis tools are being adopted by major hospitals and research institutions worldwide, advancing to the level of analyzing 30,000 genes within 48 hours in a single analysis.
In Korea, Yuhan Corporation is playing a leading role in building AI-based precision medicine platforms. Headquartered in Seoul, Yuhan launched the ‘Y-Precision’ platform through its subsidiary Yuhan Medica in 2024. This AI system comprehensively analyzes patients’ genetic information, clinical data, and lifestyle patterns to propose optimal treatment methods and is currently running pilot programs at five major hospitals, including Seoul National University Hospital and Samsung Seoul Hospital. Particularly in the field of cancer treatment, the platform achieves an 85% accuracy rate in predicting patient-specific treatment responses, reducing unnecessary side effects and enhancing treatment efficacy.
In the global pharmaceutical industry, Switzerland’s Roche is at the forefront of AI-based personalized treatment. Through its subsidiary Foundation Medicine, Roche offers comprehensive genomic profiling (CGP) services and plans to build the capacity to process 100,000 genomic analyses per month in 50 countries worldwide by 2025. Roche’s AI algorithms optimize prescriptions for cancer drugs such as ‘Tecentriq’ and ‘Herceptin,’ improving patient-specific treatment response rates by 30-40%, which is projected to generate approximately $1.5 billion in additional annual revenue.
Johnson & Johnson (J&J) in the USA is focusing on optimizing clinical trials using AI through its subsidiary Janssen. Utilizing the ‘Clinical Trial Optimization Platform’ developed in partnership with IBM in 2024, J&J has reduced the average patient recruitment period for clinical trials by 40%. This platform analyzes electronic medical record (EMR) data to automatically identify eligible clinical trial patients and predict regional patient distribution to select optimal clinical trial centers. Currently, 65% of J&J’s clinical trials utilize this AI platform, saving approximately $500 million in annual R&D costs.
Smart Manufacturing and Quality Control in Biomanufacturing
The adoption of AI technology is rapidly progressing in the biopharmaceutical manufacturing sector. Traditional biopharmaceutical production involves complex biological processes with high quality variability and challenging yield predictions, but smart biofactories combining AI and IoT sensors are addressing these issues. The proportion of AI solutions in the global biomanufacturing market is expected to surge from 8% in 2024 to 15% in 2025, representing a market size of approximately $4.5 billion. AI utilization is increasing in areas such as real-time process monitoring, predictive maintenance, and automated quality control.
Samsung Biologics possesses the highest level of technology in Asia in this field. All four plants located in the Songdo Biocluster operate with AI-based integrated management systems, boasting a total production capacity of 360,000 liters. The fourth plant, in particular, has established a system that automatically adjusts optimal culture conditions by analyzing thousands of data points generated during the cell culture process in real-time. This has reduced batch yield variability from the previous ±15% to within ±5% and improved overall production efficiency by 25%.
In the global pharmaceutical industry, Switzerland’s Novartis is recognized as a leader in AI-based biomanufacturing. Since 2023, Novartis has implemented the ‘Manufacturing Intelligence Platform’ across all its biopharmaceutical production facilities, building digital twins of production processes to simulate process optimization in virtual environments. This system uses machine learning algorithms to predict process anomalies an average of 6 hours in advance, reducing production downtime by 60% and saving approximately $300 million annually.
Pfizer in the USA is leveraging its COVID-19 vaccine production experience to establish an AI-based vaccine manufacturing platform. Pfizer has implemented AI in its mRNA vaccine production processes at its Puurs plant in Belgium and Kalamazoo plant in the USA, achieving automated quality control. The AI algorithms automate the integrity testing of mRNA and the size distribution analysis of lipid nanoparticles (LNP), reducing inspection time from the previous 4 hours to 30 minutes. Based on this technology, Pfizer plans to apply the same AI platform to the production of RSV and shingles vaccines by 2025.
The biggest challenge in adopting AI in the biotechnology industry is the complexity of regulatory environments and ensuring data quality. The US FDA clarified the approval process for AI-based medical devices with the release of the ‘AI/ML-Based Software as Medical Device (SaMD) Guidance’ in 2024, but many companies remain hesitant to adopt AI due to regulatory uncertainties. The black-box nature of AI algorithms makes securing explainability in decision-making processes a critical issue. In Europe, the AI Act, set to be enforced from 2025, will impose strict regulations on high-risk AI systems, necessitating additional investments from biotech companies to ensure regulatory compliance.
Data quality and standardization issues are also major barriers to AI adoption. Data generated during biopharmaceutical development and production comes in various forms and formats, requiring significant preprocessing for use in AI model training. Integrating and standardizing data from different equipment and systems is costly and time-consuming. To address this, the International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human Use (ICH) proposed standardization measures for biopharmaceutical data through the ‘ICH M12 Guideline’ in 2024, which major pharmaceutical companies are actively adopting.
From an investment perspective, the AI-based biotechnology sector is expected to continue growing in 2025. Global venture capital firms invested a total of $18 billion in AI biotech startups in 2024, a 25% increase from the previous year. Investments are particularly concentrated in drug discovery, precision medicine, and digital healthcare, with Korea’s K-Bio policy and private investments supporting the growth of related companies. Samsung Biologics recorded sales of 3.5 trillion won in 2024, a 15% increase from the previous year, and is expected to continue its growth with the expansion of AI-based services in 2025.
The role of AI in the biotechnology industry is expected to expand further in the future. AI utilization is anticipated to increase in new fields such as multi-omics data analysis, synthetic biology, and cell therapy development. Additionally, trends such as molecular simulation through the combination of quantum computing and AI, the spread of cloud-based AI platforms, and the advancement of personalized treatment through real-time patient monitoring are expected to emerge. These changes are expected to reshape the competitive landscape of the biotechnology industry and further strengthen the market dominance of companies with AI capabilities.
