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}.
Molecular Machine Learning in Chemical Process Design
Process Systems Engineering (AVT.SVT), RWTH Aachen University（RWTH亚琛大学过程系统工程系）；Laboratory of Artificial Chemical Intelligence (LIAC), Institute of Chemical Sciences and Engineering, EPFL（瑞士联邦理工学院（EPFL）化学科学与工程学院人工智能化学实验室）；Institute of Climate and Energy Systems ICE-1: Energy Systems Engineering, Forschungszentrum Jülich GmbH（茹里希研究中心（Jülich）气候与能源系统研究所）；Lehrstuhl Informatik 7, RWTH Aachen University（RWTH亚琛大学计算机科学教授职位7号）；National Centre of Competence in Research (NCCR) Catalysis, EPFL（瑞士联邦理工学院（EPFL）催化国家竞争力研究中心）；JARA Center for Simulation and Data Science (CSD)（JARA模拟与数据科学中心）
MolKD: Distilling Cross-Modal Knowledge in Chemical Reactions for Molecular Property Prediction
Institute for Interdisciplinary Information Sciences (IIIS), Tsinghua University（清华大学交叉信息研究院）；Zhejiang Lab（之江实验室）；The Chinese University of Hong Kong（香港中文大学）
GeoT: A Geometry-aware Transformer for Reliable Molecular Property Prediction and Chemically Interpretable Representation Learning
Kakao Corporation（Kakao公司）；LG Chem（LG化学）；Seoul National University（首尔国立大学）；Korea Advanced Institute of Science and Technology (KAIST)（韩国科学技术院（KAIST））；Seoul National University of Science and Technology（首尔国立科技大学）
Efficient Chemical Space Exploration Using Active Learning Based on Marginalized Graph Kernel: an Application for Predicting the Thermodynamic Properties of Alkanes with Molecular Simulation
Shanghai Jiao Tong University（上海交通大学）；Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）；Purdue University（普渡大学）
The PubChemQC Project: a large chemical database from the first principle calculations
在本研究中，我们通过{ \it ab initio}计算构建了一个大型的分子数据库。目前，我们已经有超过153万的6- 31 G * B3 LYP优化几何和10个激发态的6-31+G* TDDFT计算。在计算分子时，我们只参考国际纯粹与应用化学联合会（IUPAC）对化学式的InChI（国际化学标识符）表示，因此没有参考实验数据。这些结果在http://pubchemqc.riken.jp/上向公众开放。分子数据取自PubChem项目（http：//pubchem.ncbi.nlm.nih.gov/），该项目是世界上最大的（列出了约6300万个分子）和免费（公共领域）数据库之一。我们的最终目标是，利用这些数据，开发一个分子搜索引擎或分子专家系统，以找到具有所需特性的分子。
IMPRESSION -- Prediction of NMR Parameters for 3-dimensional chemical structures using Machine Learning with near quantum chemical accuracy
IMPRESSION（Intelligent Machine Prediction of Shift and Scalar Information Of Nuclei）机器学习系统为从三维化学结构预测NMR参数提供了一条高效准确的途径。在这里，我们证明了在NMR参数的量子化学计算值上训练的机器学习预测基本上与量子化学计算（每个分子数小时/天）一样准确，但计算效率更高（每个分子数十毫秒）。在量子化学而不是实验数据上训练机器学习系统，避免了对大型，结构多样，无错误的实验数据库存在的需求，并使IMPRESSION适用于解决分子构象和异构性等三维问题
Revving up 13C NMR shielding predictions across chemical space: Benchmarks for atoms-in-molecules kernel machine learning with new data for 134 kilo molecules
