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Molecular Conformer Search with Low-Energy Latent Space.

ABSTRACT: 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.

SUBMITTER: Guo X

PROVIDER: S-EPMC9281398 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature

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Molecular Conformer Search with Low-Energy Latent Space.

Guo Xiaomi X Fang Lincan L Xu Yong Y Duan Wenhui W Rinke Patrick P Todorović Milica M Chen Xi X

Journal of chemical theory and computation 20220613 7

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 t ...[more]

PMID: 35696366

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