qcMol: a large-scale dataset of 1.2 million molecules with high-quality quantum chemical annotations for molecular representation learning

Recent advancements in deep learning have greatly prompted the de novo design of drugs and materials. Previous studies have shown that a well-designed molecular representation is critical for improving the accuracy of deep-learning-based molecular property prediction methods. However, the lack of large-scale data enriched with detailed physicochemical information hinders effective learning of an informative molecular representation. To fill this data gap, we introduce qcMol, a dataset consisting of 1.2 million molecules from 95 datasets with high-quality quantum chemical annotations, to facilitate molecular representation learning as well as downstream molecular property prediction. Chemicals in this dataset include drug-like compounds, metabolites and molecules with matched experimental data, covering 247,448 kinds of scaffolds and a broad spectrum of molecular sizes. Each compound in qcMol is annotated with detailed quantum chemical information, obtained through reliable quantum chemical calculations based on B3LYP-D3/def2-SV(P)//GFN2-xTB as well as the follow-up wave function post-analysis. These features are organized into multiple formats, allowing for flexible integration into diversified molecular representation learning frameworks. The broad data distribution, comprehensive quantum chemical annotations and flexible data formats jointly enable qcMol to serve as the pre-training resource as well as the benchmark test set for deep learning models, benefiting the practical in silico drug discovery. qcMol is freely accessible from https://structpred.life.tsinghua.edu.cn/qcmol/