Knowledge Distillation for Molecular Property Prediction: A Scalability Analysis (Adv. Sci. 22/2025)

Knowledge Distillation for Molecular Property Prediction This illustration shows how AI distills rich molecular insights from complex teacher models into compact student models, enabling accurate, scalable prediction of both experimental and quantum mechanical properties—advancing molecular design and discovery across domains. More details can be found in article number 2503271 by Rahul Sheshanarayana and Fengqi You.