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Machine learned coarse-grained protein force-fields: Are we there yet?

  • 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.

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Author:Aleksander Durumeric, Nicholas Charron, Clark Templeton, Félix Musil, Klara Bonneau, Aldo Pasos-Trejo, Yaoyi Chen, Atharva Kelkar, Frank Noé, Cecilia Clementi
Document Type:Article
Parent Title (English):Current Opinion in Structural Biology
Year of first publication:2023
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