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, $O\left(n^4\right)$ 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.

估计分子中每个原子的重要性是化学，物理学和材料科学中最吸引人和最具挑战性的问题之一。估计原子重要性的最常见方法是使用密度泛函理论（DFT）计算电子结构，然后使用人类专家的领域知识对其进行解释。但是，这种常规方法对大型分子数据库不切实际，因为DFT计算需要大量计算，特别是，$Ø（{ñ}^4）$时间复杂度与电子基础功能的数量有关。此外，计算结果应由人类专家手动解释，以根据目标分子特性估算原子的重要性。为了解决这个问题，我们首先利用基于机器学习的方法，基于图神经网络上的反向自注意力将原子重要性估计，并将其与基于图的分子描述相集成。我们的方法无需任何化学和物理学领域的知识即可提供一种有效的自动化方法，并以目标为导向来估算原子的重要性。