Repository: Freie Universität Berlin, Math Department

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

Durumeric, Alexander E.P. and Charron, Nicholas E. and Templeton, Clark and Musil, Félix and Bonneau, Klara and Pasos-Trejo, Aldo S. and Chen, Yaoyi and Kelkar, Atharva and Noé, Frank and Clementi, Cecilia (2023) Machine learned coarse-grained protein force-fields: Are we there yet? Current Opinion in Structural Biology, 79 .

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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.

Item Type:Article
Subjects:Mathematical and Computer Sciences
Mathematical and Computer Sciences > Mathematics
Mathematical and Computer Sciences > Mathematics > Applied Mathematics
Divisions:Department of Mathematics and Computer Science > Institute of Mathematics
ID Code:2952
Deposited By: Monika Drueck
Deposited On:20 Apr 2023 08:11
Last Modified:20 Apr 2023 08:11

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