01255nas a2200265   4500000000100000000000100001008004100002260001500043653002500058653001300083100002500096700001900121700002400140700001900164700001500183700002800198700001700226700002500243245010000268856005500368300000800423490000600431520053800437022001400975       2025                            d  c2025-11-1710aComputational models10aDiseases1 aFrancesca Pistollato1 aFabia Furtmann1 aLindsay J. Marshall1 aSurat Parvatam1 aJan Turner1 aFlora Tshinanu Musuamba1 aGiulia Russo1 aFrancesco Pappalardo00aAdvancing the frontier of rare disease modeling: a critical appraisal of in silico technologies  uhttps://www.nature.com/articles/s41746-025-02068-1  a6760 v83 aRare 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.  a2398-6352