| Literature DB >> 33606945 |
Tamar Schlick1,2,3, Stephanie Portillo-Ledesma1, Christopher G Myers1, Lauren Beljak4, Justin Chen4, Sami Dakhel4, Daniel Darling4, Sayak Ghosh4, Joseph Hall4, Mikaeel Jan4, Emily Liang4, Sera Saju4, Mackenzie Vohr4, Chris Wu4, Yifan Xu4, Eva Xue4.
Abstract
We reassess progress in the field of biomolecular modeling and simulation, following up on our perspective published in 2011. By reviewing metrics for the field's productivity and providing examples of success, we underscore the productive phase of the field, whose short-term expectations were overestimated and long-term effects underestimated. Such successes include prediction of structures and mechanisms; generation of new insights into biomolecular activity; and thriving collaborations between modeling and experimentation, including experiments driven by modeling. We also discuss the impact of field exercises and web games on the field's progress. Overall, we note tremendous success by the biomolecular modeling community in utilization of computer power; improvement in force fields; and development and application of new algorithms, notably machine learning and artificial intelligence. The combined advances are enhancing the accuracy andscope of modeling and simulation, establishing an exemplary discipline where experiment and theory or simulations are full partners.Entities:
Keywords: DNA folding; RNA folding; artificial intelligence; biomolecular dynamics; biomolecular modeling; biomolecular simulation; citizen science projects; machine learning; multiscale modeling; protein folding; structure prediction
Mesh:
Year: 2021 PMID: 33606945 PMCID: PMC8105287 DOI: 10.1146/annurev-biophys-091720-102019
Source DB: PubMed Journal: Annu Rev Biophys ISSN: 1936-122X Impact factor: 12.981