Efficient Amino Acid Conformer Search with Bayesian Optimization

Lincan Fang, Esko Makkonen, Milica Todorović, Patrick Rinke*, Xi Chen

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

6 Citations (Scopus)
79 Downloads (Pure)

Abstract

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.

Original languageEnglish
Pages (from-to)1955–1966
Number of pages12
JournalJournal of Chemical Theory and Computation
Volume17
Issue number3
DOIs
Publication statusPublished - 9 Mar 2021
MoE publication typeA1 Journal article-refereed

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