Predicting accurate ab initio DNA electron densities with equivariant neural networks.

ABSTRACT: One of the fundamental limitations of accurately modeling biomolecules like DNA is the inability to perform quantum chemistry calculations on large molecular structures. We present a machine learning model based on an equivariant Euclidean neural network framework to obtain accurate ab initio electron densities for arbitrary DNA structures that are much too large for conventional quantum methods. The model is trained on representative B-DNA basepair steps that capture both base pairing and base stacking interactions. The model produces accurate electron densities for arbitrary B-DNA structures with typical errors of less than 1%. Crucially, the error does not increase with system size, which suggests that the model can extrapolate to large DNA structures with negligible loss of accuracy. T

SUBMITTER: Lee AJ 

PROVIDER: S-EPMC9674991 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature