Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics simulations on larger systems achieving ergodic sampling is paving the way to directly using such simulations along with solution experiments obtained on macromolecular systems. Recently, a number of methods have been introduced to automatize this approach. Here, we review these methods, highlight their relationship with machine learning methods, and discuss the open challenges in the field.

Toward empirical force fields that match experimental observables / Froehlking, Thorben; Bernetti, Mattia; Calonaci, Nicola; Bussi, Giovanni. - In: THE JOURNAL OF CHEMICAL PHYSICS. - ISSN 0021-9606. - 152:23(2020), pp. 1-9. [10.1063/5.0011346]

Toward empirical force fields that match experimental observables

Froehlking, Thorben;Bernetti, Mattia;Calonaci, Nicola;Bussi, Giovanni
2020-01-01

Abstract

Biomolecular force fields have been traditionally derived based on a mixture of reference quantum chemistry data and experimental information obtained on small fragments. However, the possibility to run extensive molecular dynamics simulations on larger systems achieving ergodic sampling is paving the way to directly using such simulations along with solution experiments obtained on macromolecular systems. Recently, a number of methods have been introduced to automatize this approach. Here, we review these methods, highlight their relationship with machine learning methods, and discuss the open challenges in the field.
2020
152
23
1
9
230902
https://aip.scitation.org/doi/10.1063/5.0011346
Froehlking, Thorben; Bernetti, Mattia; Calonaci, Nicola; Bussi, Giovanni
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11767/112969
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