Advancing the frontier of rare disease modeling: a critical appraisal of in silico technologies

Abstract

Rare diseases affect over 300 million people worldwide and pose unique research challenges. In silico approaches, such as mechanistic models, machine learning, and simulations, offer scalable tools for disease characterisation, drug discovery, and virtual trials. This review categorises these methods by context of use, critically appraises their strengths and limitations, and identifies barriers to translation, highlighting key opportunities and ongoing challenges in advancing computational strategies for rare disease research.