(218e) Applications of Machine-Learned Electron Densities of Nucleic Acids

The efficient prediction of large-scale electron densities of biological macromolecules and molecular arrays could lead to breakthroughs in quantum chemistry, structural biology, and molecular design. This work leverages a novel machine learning (ML) algorithm termed Euclidean Neural Networks to learn the electron densities of arbitrary sequences of DNA by performing many smaller ab initio calculations of the component base-pair steps of DNA. These electron density predictions are currently accurate to around 1% error for arbitrary sequences of DNA, and are easily size-extensible unlike traditional quantum calculations (Lee et al. 2022). Using this machine learning algorithm, electron density predictions of large-scale DNA structures consisting of tens-of-thousands to hundreds-of-thousands of electrons can be performed in minutes. Several applications of these large-scale machine-learned electron densities are of interest. We focus here on the accurate calculation of energies and forces from the machine-learned electron densities, which allows for dynamical propagation of the electronic system, termed ab initio molecular dynamics (AIMD). These machine-learned electron densities would allow for AIMD simulations of systems that are much larger than can be propagated using traditional quantum calculations. In addition, these forces can be utilized to develop more accurate force fields for classical molecular dynamics. Other applications such as the use of machine-learned electron densities to aid in X-ray crystal refinement of large biomolecular structures will be discussed in brief.

AJ Lee, JA Rackers, WP Bricker. Predicting quantum-accurate electron densities for DNA with equivariant neural networks. ChemRxiv (10 Mar 2022).