Reverse graph self-attention for target-directed atomic importance estimation

Abstract

Estimating the importance of each atom in a molecule is one of the most appealing and challenging problems in chemistry, physics, and materials science. The most common way to estimate the atomic importance is to compute the electronic structure using density functional theory (DFT), and then to interpret it using domain knowledge of human experts. However, this conventional approach is impractical to the large molecular database because DFT calculation requires large computation, specifically, O(n4) time complexity w.r.t. the number of electronic basis functions. Furthermore, the calculation results should be manually interpreted by human experts to estimate the atomic importance in terms of the target molecular property. To tackle this problem, we first exploit the machine learning-based approach for the atomic importance estimation based on the reverse self-attention on graph neural networks and integrating it with graph-based molecular description. Our method provides an efficiently-automated and target-directed way to estimate the atomic importance without any domain knowledge of chemistry and physics.