A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
Molecular Conformer Search with Low-Energy Latent Space
Tekijät: Guo Xiaomi, Fang Lincan, Xu Yong, Duan Wenhui, Rinke Patrick, Todorović Milica, Chen Xi
Kustantaja: AMER CHEMICAL SOC
Julkaisuvuosi: 2022
Journal: Journal of Chemical Theory and Computation
Tietokannassa oleva lehden nimi: JOURNAL OF CHEMICAL THEORY AND COMPUTATION
Lehden akronyymi: J CHEM THEORY COMPUT
Vuosikerta: 18
Numero: 7
Aloitussivu: 4574
Lopetussivu: 4585
Sivujen määrä: 12
ISSN: 1549-9618
DOI: https://doi.org/10.1021/acs.jctc.2c00290
Verkko-osoite: https://doi.org/10.1021/acs.jctc.2c00290
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/176010810
Identifying low-energy conformers with quantum mechanical accuracy for molecules with many degrees of freedom is challenging. In this work, we use the molecular dihedral angles as features and explore the possibility of performing molecular conformer search in a latent space with a generative model named variational auto-encoder (VAE). We bias the VAE towards low-energy molecular configurations to generate more informative data. In this way, we can effectively build a reliable energy model for the low-energy potential energy surface. After the energy model has been built, we extract local-minimum conformations and refine them with structure optimization. We have tested and benchmarked our low-energy latent-space (LOLS) structure search method on organic molecules with 5-9 searching dimensions. Our results agree with previous studies.
Ladattava julkaisu This is an electronic reprint of the original article. |  |
