Molecular property prediction is an important problem in drug discovery and
materials science. As geometric structures have been demonstrated necessary for
molecular property prediction, 3D information has been combined with various
graph learning methods to boost prediction performance. However, obtaining the
geometric structure of molecules is not feasible in many real-world
applications due to the high computational cost. In this work, we propose a
novel 3D pre-training framework (dubbed 3D PGT), which pre-trains a model on 3D
molecular graphs, and then fine-tunes it on molecular graphs without 3D
structures. Based on fact that bond length, bond angle, and dihedral angle are
three basic geometric descriptors corresponding to a complete molecular 3D
conformer, we first develop a multi-task generative pre-train framework based
on these three attributes. Next, to automatically fuse these three generative
tasks, we design a surrogate metric using the \textit{total energy} to search
for weight distribution of the three pretext task since total energy
corresponding to the quality of 3D conformer.Extensive experiments on 2D
molecular graphs are conducted to demonstrate the accuracy, efficiency and
generalization ability of the proposed 3D PGT compared to various pre-training
baselines.

本文提出了一个名为 3D PGT 的新颖框架，它可以在无法获得几何结构的实际应用情况下，以 2D 结构为输入预测分子属性，实验证明与各种预训练基线相比，其具有更高的准确性，效率和泛化能力。