Overview Statistic: PDF-Downloads (blue) and Frontdoor-Views (gray)
  • search hit 3 of 5
Back to Result List

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.

Export metadata

Additional Services

Share in Twitter Search Google Scholar Statistics - number of accesses to the document
Metadaten
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
Volume:79
Year of first publication:2023
DOI:https://doi.org/10.1016/j.sbi.2023.102533
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.