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<p>An informative molecular representation is prerequisite for the accurate prediction of molecular property by machine learning, but demands large-scale data enriched with detailed physicochemical information for its effective learning. Here, we introduce qcMol, a dataset consisting of 1.2 million molecules with DFT-level quantum chemical annotations, to facilitate molecular representation learning. 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 multiple quantum descriptors, obtained through reliable quantum chemical calculations at the level of 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. qcMol can serve as not only the pre-training resource but also the benchmark test set for machine learning models, benefiting the practical in silico drug discovery.</p><p></p>

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A dataset of 1.2 million molecules with DFT-level quantum chemical annotations for molecular representation learning

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  • 出版模式：

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  • 发表日期：

    2026年5月26日

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  • Haoyu Wang,
  • Ziyan Zhang,
  • Haipeng Gong

摘要

An informative molecular representation is prerequisite for the accurate prediction of molecular property by machine learning, but demands large-scale data enriched with detailed physicochemical information for its effective learning. Here, we introduce qcMol, a dataset consisting of 1.2 million molecules with DFT-level quantum chemical annotations, to facilitate molecular representation learning. 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 multiple quantum descriptors, obtained through reliable quantum chemical calculations at the level of 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. qcMol can serve as not only the pre-training resource but also the benchmark test set for machine learning models, benefiting the practical in silico drug discovery.