TY - JOUR A1 - Durumeric, Aleksander A1 - Charron, Nicholas A1 - Templeton, Clark A1 - Musil, Félix A1 - Bonneau, Klara A1 - Pasos-Trejo, Aldo A1 - Chen, Yaoyi A1 - Kelkar, Atharva A1 - Noé, Frank A1 - Clementi, Cecilia T1 - Machine learned coarse-grained protein force-fields: Are we there yet? T2 - Current Opinion in Structural Biology N2 - The successful recent application of machine learning methods to scientific problems includes the learning of flexible and accurate atomic-level force-fields for materials and biomolecules from quantum chemical data. In parallel, the machine learning of force-fields at coarser resolutions is rapidly gaining relevance as an efficient way to represent the higher-body interactions needed in coarse-grained force-fields to compensate for the omitted degrees of freedom. Coarse-grained models are important for the study of systems at time and length scales exceeding those of atomistic simulations. However, the development of transferable coarse-grained models via machine learning still presents significant challenges. Here, we discuss recent developments in this field and current efforts to address the remaining challenges. Y1 - 2023 UR - https://opus4.kobv.de/opus4-zib/frontdoor/index/index/docId/9347 VL - 79 ER -