LAGNet: better electron density prediction for LCAO-based data and drug-like substances

Abstract

The electron density is an important object in quantum chemistry that is crucial for many downstream tasks in drug design. Recent deep learning approaches predict the electron density around a molecule from atom types and atom positions. Most of these methods use the plane wave (PW) numerical method as a source of ground-truth training data. However, the drug design field mostly uses the Linear Combination of Atomic Orbitals (LCAO) for computation of quantum properties. In this study, we focus on prediction of the electron density for drug-like substances and training neural networks with LCAO-based datasets. Our experiments show that proper handling of large amplitudes of core orbitals is crucial for training on LCAO-based data. We propose to store the electron density with the standard grids instead of the uniform grid. This allowed us to reduce the number of probing points per molecule by 43 times and reduce storage space requirements by 8 times. Finally, we propose a novel architecture based on the DeepDFT model that we name LAGNet. It is specifically designed and tuned for drug-like substances and DFT dataset.Scientific contribution We propose a core suppression model to correctly handle core orbitals and train neural network on LCAO-based data with atoms of the 3rd and 4th periods. We show that using the standard grid instead of the uniform grid drastically reduces the number of electron density probing points and data storage requirements. Finally, we propose the LAGNet model that allows to get better results on drug-like substances than the equivariant DeepDFT model.Graphical abstract