  Molecular representation learning is pivotal for various molecular property
prediction tasks related to drug discovery. Robust and accurate benchmarks are
essential for refining and validating current methods. Existing molecular
property benchmarks derived from wet experiments, however, face limitations
such as data volume constraints, unbalanced label distribution, and noisy
labels. To address these issues, we construct a large-scale and precise
molecular representation dataset of approximately 140,000 small molecules,
meticulously designed to capture an extensive array of chemical, physical, and
biological properties, derived through a robust computational ligand-target
binding analysis pipeline. We conduct extensive experiments on various deep
learning models, demonstrating that our dataset offers significant
physicochemical interpretability to guide model development and design.
Notably, the dataset's properties are linked to binding affinity metrics,
providing additional insights into model performance in drug-target interaction
tasks. We believe this dataset will serve as a more accurate and reliable
benchmark for molecular representation learning, thereby expediting progress in
the field of artificial intelligence-driven drug discovery.
