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.
| 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 |
