As quantum chemical properties have a significant dependence on their
geometries, graph neural networks (GNNs) using 3D geometric information have
achieved high prediction accuracy in many tasks. However, they often require 3D
geometries obtained from high-level quantum mechanical calculations, which are
practically infeasible, limiting their applicability in real-world problems. To
tackle this, we propose a method to accurately predict the properties with
relatively easy-to-obtain geometries (e.g., optimized geometries from the
molecular force field). In this method, the input geometry, regarded as the
corrupted geometry of the correct one, gradually approaches the correct one as
it passes through the stacked denoising layers. We investigated the performance
of the proposed method using 3D message-passing architectures for two
prediction tasks: molecular properties and chemical reaction property. The
reduction of positional errors through the denoising process contributed to
performance improvement by increasing the mutual information between the
correct and corrupted geometries. Moreover, our analysis of the correlation
between denoising power and predictive accuracy demonstrates the effectiveness
of the denoising process.

本文提出了采用分层去噪方法预测量子化学性质的方法，以优化几何结构为输入，解决高级量子力学计算的不可行性问题，实现了在两个预测任务中取得了较高的准确性。