  Recent developments in deep learning have made remarkable progress in
speeding up the prediction of quantum chemical (QC) properties by removing the
need for expensive electronic structure calculations like density functional
theory. However, previous methods learned from 1D SMILES sequences or 2D
molecular graphs failed to achieve high accuracy as QC properties primarily
depend on the 3D equilibrium conformations optimized by electronic structure
methods, far different from the sequence-type and graph-type data. In this
paper, we propose a novel approach called Uni-Mol+ to tackle this challenge.
Uni-Mol+ first generates a raw 3D molecule conformation from inexpensive
methods such as RDKit. Then, the raw conformation is iteratively updated to its
target DFT equilibrium conformation using neural networks, and the learned
conformation will be used to predict the QC properties. To effectively learn
this update process towards the equilibrium conformation, we introduce a
two-track Transformer model backbone and train it with the QC property
prediction task. We also design a novel approach to guide the model's training
process. Our extensive benchmarking results demonstrate that the proposed
Uni-Mol+ significantly improves the accuracy of QC property prediction in
various datasets. We have made the code and model publicly available at
\url{https://github.com/dptech-corp/Uni-Mol}.
