Lu Shuqi, Gao Zhifeng, He Di, Zhang Linfeng, Ke Guolin
DP Technology, Beijing, China.
Peking University, Beijing, China.
Nat Commun. 2024 Aug 19;15(1):7104. doi: 10.1038/s41467-024-51321-w.
Quantum chemical (QC) property prediction is crucial for computational materials and drug design, but relies on expensive electronic structure calculations like density functional theory (DFT). Recent deep learning methods accelerate this process using 1D SMILES or 2D graphs as inputs but struggle to achieve high accuracy as most QC properties depend on refined 3D molecular equilibrium conformations. We introduce Uni-Mol+, a deep learning approach that leverages 3D conformations for accurate QC property prediction. Uni-Mol+ first generates a raw 3D conformation using RDKit then iteratively refines it towards DFT equilibrium conformation using neural networks, which is finally used to predict the QC properties. To effectively learn this conformation update process, we introduce a two-track Transformer model backbone and a novel training approach. Our benchmarking results demonstrate that the proposed Uni-Mol+ significantly improves the accuracy of QC property prediction in various datasets.
量子化学（QC）性质预测对于计算材料和药物设计至关重要，但依赖于诸如密度泛函理论（DFT）等昂贵的电子结构计算。最近的深度学习方法使用一维SMILES或二维图形作为输入来加速这一过程，但由于大多数QC性质依赖于精细的三维分子平衡构象，因此难以实现高精度。我们引入了Uni-Mol+，这是一种利用三维构象进行精确QC性质预测的深度学习方法。Uni-Mol+首先使用RDKit生成一个原始的三维构象，然后使用神经网络将其迭代优化为DFT平衡构象，最终用于预测QC性质。为了有效地学习这种构象更新过程，我们引入了一个双轨Transformer模型主干和一种新颖的训练方法。我们的基准测试结果表明，所提出的Uni-Mol+显著提高了各种数据集中QC性质预测的准确性。
