Efficient Amino Acid Conformer Search with Bayesian Optimization

Finding low-energy molecular conformers is challenging due to the high dimensionality of the search space and the computational cost of accurate quantum chemical methods for determining conformer structures and energies. Here, we combine active-learning Bayesian optimization (BO) algorithms with quantum chemistry methods to address this challenge. Using cysteine as an example, we show that our procedure is both efficient and accurate. After only 1000 single-point calculations and approximately 80 structure relaxations, which is less than 10% computational cost of the current fastest method, we have found the low-energy conformers in good agreement with experimental measurements and reference calculations. To test the transferability of our method, we also repeated the conformer search of serine, tryptophan, and aspartic acid. The results agree well with previous conformer search studies.

Medienart:

E-Artikel

Erscheinungsjahr:

2021

Erschienen:

2021

Enthalten in:

Zur Gesamtaufnahme - volume:17

Enthalten in:

Journal of chemical theory and computation - 17(2021), 3 vom: 09. März, Seite 1955-1966

Sprache:

Englisch

Beteiligte Personen:

Fang, Lincan [VerfasserIn]
Makkonen, Esko [VerfasserIn]
Todorović, Milica [VerfasserIn]
Rinke, Patrick [VerfasserIn]
Chen, Xi [VerfasserIn]

Links:

Volltext

Themen:

Amino Acids
Journal Article

Anmerkungen:

Date Completed 05.07.2021

Date Revised 05.07.2021

published: Print-Electronic

Citation Status MEDLINE

doi:

10.1021/acs.jctc.0c00648

funding:

Förderinstitution / Projekttitel:

PPN (Katalog-ID):

NLM32137178X