Severance Hospital is establishing an AI-based digital preclinical platform to accelerate the development of treatments and vaccines for emerging infectious diseases. The goal is to predict and validate the efficacy and safety of candidate substances in the preclinical stage, thereby shortening the drug development timeline and increasing the likelihood of success.
On August 4, Professor Nam Ki-taek of the Department of Biomedical Science at Yonsei University announced that his research team has been selected for a new project under the Ministry of Science and ICT's 2026 Bio-Medical Technology Development Program (AI Bio New Drug Sector).
The project, titled 'Establishment of a Next-Generation Infectious Disease Response Data-Verification Linked Fast-Track System and Operation of a Preclinical Platform,' will receive a total of 10.8 billion won in research funding over four and a half years, from July 2026 to December 2030.
The research team plans to standardize preclinical data necessary for the development of infectious disease treatments and vaccines, creating a digital preclinical decision-making system that integrates AI-based predictions with actual preclinical experiments. They will establish a data value chain (DVC) for the collection, refinement, and analysis of pharmacokinetic, efficacy, safety, and immunogenicity data, along with an AI-capable preclinical data infrastructure.
The platform will operate by having AI first predict the pharmacokinetics, distribution, toxicity, efficacy, and immunogenicity of candidate substances, which will then be compared and validated against actual preclinical experimental results. The potential for clinical entry will be assessed for drug candidates based on analyses of drug exposure, distribution, and toxicity, while vaccine candidates will be evaluated for their immunogenicity and immune response.
Additionally, an AI-based clinical translation system utilizing preclinical data and physiological parameters will be established. This system will predict the therapeutic effects in actual clinical settings based on animal experiment results, helping to prioritize candidate substances and support subsequent development strategies.
Professor Jeong Soo-jin from the Department of Infectious Diseases at Yonsei University will review the clinical applicability of the preclinical research results and participate in the development strategies for treatments and vaccines.
The research will be conducted in phases. In Phase 1 (2026-2028), the team will secure new datasets related to infectious disease treatments and vaccines, establish an AI-based data infrastructure, and create a fast-track system for infectious disease response. Phase 2 (2029-2030) will focus on validating the platform using experimental data from treatment and vaccine candidates and enhancing intelligent preclinical prediction services by integrating large language models (LLM) and retrieval-augmented generation (RAG) technology.
Drug development is a long-term endeavor, often taking over a decade. The research team aims to shorten the decision-making process from candidate substance discovery to preclinical efficacy and safety evaluation, and clinical entry prediction during outbreaks of new infectious diseases. They plan to link the established preclinical data and AI models with the national bio data infrastructure to expand public research capabilities.
Professor Nam Ki-taek stated, "This research is significant in establishing a new infectious disease research platform that connects preclinical data, AI predictions, and experimental validation into a single decision-making system. We aim to complete a digital preclinical platform that can evaluate treatment and vaccine candidates more quickly and accurately during outbreaks of new infectious diseases."
* This article has been translated by AI.
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