A groundbreaking artificial intelligence (AI)-powered digital preclinical platform is set to revolutionize the development of treatments and vaccines for emerging infectious diseases. This innovative system will harness artificial intelligence to predict the efficacy and safety of potential drug candidates, which will then be validated through rigorous laboratory experiments. The ultimate goal is to significantly reduce drug development timelines while boosting the chances of success.

Severance Hospital announced on Tuesday that a research team led by Professor Nam Ki-taek from Yonsei University College of Medicine has secured funding for a cutting-edge project in the AI bio sector. This initiative is part of the Ministry of Science and Information and Communications Technology's (ICT) 2026 Bio-Medical Technology Development Project.

The ambitious research project, titled, Establishment of a Fast-Track System for Data-Verification Linkage in Next-Generation Infectious Disease Response and Operation of Preclinical Platforms, has been awarded a substantial grant of 10.8 billion KRW (approximately 7.5 million USD). The project commenced last month and is slated to run until December 2030, spanning four and a half years.

At the heart of this initiative is the creation of a sophisticated digital preclinical decision-making system. This system will standardize crucial preclinical data essential for developing treatments and vaccines against infectious diseases. It will seamlessly integrate AI predictions with actual preclinical experiments, streamlining the entire process.

This approach marks a significant departure from traditional methods, where researchers painstakingly validated each treatment and vaccine candidate through individual preclinical experiments. The new AI-driven system will prioritize candidates with the highest likelihood of success, optimizing the testing process.

To achieve this, the research team will develop a comprehensive system for data collection, refinement, and analysis. They will standardize data on pharmacokinetics, bodily distribution, toxicity, efficacy, and immunogenicity of candidate substances. This will form the foundation of an AI-compatible preclinical data infrastructure.

The platform's core functionality revolves around AI's ability to predict the development potential of treatment and vaccine candidates. These predictions will then be rigorously compared and validated against actual preclinical experimental results. For treatment candidates, the team will assess clinical entry potential by analyzing drug exposure, distribution, toxicity, and efficacy. Vaccine candidates will be evaluated based on their immunogenicity and immune response profiles.

In addition to this, the team will develop an AI-powered clinical translational system. This system will leverage preclinical data and physiological parameters to predict therapeutic effects in real-world clinical settings. By analyzing animal test results, it will help prioritize candidate substances and inform subsequent development strategies.

Professor Jeong Soo-jin, a renowned infectious disease specialist at Yonsei University Medical School, will play a crucial role in examining the clinical relevance of preclinical research outcomes. Her expertise will be instrumental in shaping strategies for treatment and vaccine development.

The research project is structured in two distinct phases. The first phase, spanning from 2026 to 2028, will focus on building the foundation. This includes securing new datasets related to infectious disease treatments and vaccines, establishing an AI-based data infrastructure, and developing a fast-track system for responding to infectious diseases.

The second phase, scheduled for 2029-2030, will concentrate on refining and validating the platform. The team will use experimental data from candidate substances to test the system's accuracy. They also plan to enhance the platform's capabilities by incorporating advanced technologies such as large language models (LLM) and retrieval-augmented generation (RAG).

The research team is optimistic about the platform's potential impact. They anticipate that it will significantly accelerate the decision-making process in the event of new infectious disease outbreaks. From the discovery of candidate substances to evaluating preclinical efficacy and safety, and predicting clinical entry potential, the entire process is expected to become more efficient. This, in turn, should increase the success rates for developing treatments and vaccines. Moreover, the team plans to make the established preclinical data and AI models available as a public research resource, integrating it with the national bio data infrastructure for use by infectious disease researchers across the country.

Professor Nam emphasized the significance of this research, stating that this project represents a paradigm shift in infectious disease research. It's creating a unified decision-making system that seamlessly integrates preclinical data, AI predictions, and experimental validation. The ultimate aim is to develop a state-of-the-art digital preclinical platform capable of swiftly and accurately evaluating treatment and vaccine candidates during outbreaks of new infectious diseases, thereby enhancing their potential for clinical application.

A groundbreaking artificial intelligence (AI)-powered digital preclinical platform is set …

AI-Driven Preclinical Platform: Revolutionizing Infectious Disease Treatment and Vaccine Development by 2030