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Dynamic particle swarm optimization of biomolecular simulation parameters with flexible objective functions

Weiel, Marie ORCID iD icon 1; Götz, Markus 1; Klein, André; Coquelin, Daniel ORCID iD icon 1; Floca, Ralf; Schug, Alexander
1 Scientific Computing Center (SCC), Karlsruher Institut für Technologie (KIT)


Molecular simulations are a powerful tool to complement and interpret ambiguous experimental data on biomolecules to obtain structural models. Such data-assisted simulations often rely on parameters, the choice of which is highly non-trivial and crucial to performance. The key challenge is weighting experimental information with respect to the underlying physical model. We introduce FLAPS, a self-adapting variant of dynamic particle swarm optimization, to overcome this parameter selection problem. FLAPS is suited for the optimization of composite objective functions that depend on both the optimization parameters and additional, a priori unknown weighting parameters, which substantially influence the search-space topology. These weighting parameters are learned at runtime, yielding a dynamically evolving and iteratively refined search-space topology. As a practical example, we show how FLAPS can be used to find functional parameters for small-angle X-ray scattering-guided protein simulations.

Verlagsausgabe §
DOI: 10.5445/IR/1000136914
Veröffentlicht am 30.08.2021
DOI: 10.1038/s42256-021-00366-3
Zitationen: 18
Web of Science
Zitationen: 16
Zitationen: 23
Cover der Publikation
Zugehörige Institution(en) am KIT Scientific Computing Center (SCC)
Universität Karlsruhe (TH) – Zentrale Einrichtungen (Zentrale Einrichtungen)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 08.2021
Sprache Englisch
Identifikator ISSN: 2522-5839
KITopen-ID: 1000136914
HGF-Programm 46.21.04 (POF IV, LK 01) HAICU
Erschienen in Nature machine intelligence
Verlag Nature Research
Band 3
Heft 8
Seiten 727–734
Vorab online veröffentlicht am 22.07.2021
Nachgewiesen in Scopus
Web of Science
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