B3DB: A Curated Diverse Molecular Database of Blood-Brain Barrier Permeability with Chemical Descriptors

The highly-selective blood-brain barrier (BBB) prevents neurotoxic substances in blood from crossing into the extracellular fluid of the central nervous system (CNS). As such, the BBB has a close relationship with CNS disease development and treatment, so predicting whether a substance crosses the BBB is a key task in lead discovery for CNS drugs. Machine learning (ML) is a promising strategy for predicting the BBB permeability, but existing studies have been limited by small datasets with limitedchemical diversity. To mitigate this issue, we present a large benchmark dataset, B3DB, complied from 50 published resources and categorized based on experimental uncertainty. A subset of the molecules in B3DB has numerical logBB values (1058 compounds), while the whole dataset has categorical (BBB+ or BBB-) BBB permeability labels (7807). The dataset is freely available at https://github.com/theochem/B3DB. We also provide some physicochemical properties of the molecules. By analyzing these properties, we can demonstrate some physiochemical similarities and differences between BBB+ and BBB- compounds.高度选择性血脑屏障（blood-brain barrier, BBB）可阻断血液中的神经毒性物质侵入中枢神经系统（central nervous system, CNS）的细胞外液。因此，血脑屏障与中枢神经系统疾病的发生发展及治疗紧密相关，故而预测物质能否跨越血脑屏障，是中枢神经系统药物先导物发现过程中的核心任务。机器学习（machine learning, ML）是预测血脑屏障通透性的极具潜力的技术手段，但现有研究受限于化学多样性不足的小型数据集。为缓解这一问题，我们构建了大型基准数据集B3DB，该数据集整合了50项已发表的研究资源，并根据实验不确定性进行了分类。数据集中的部分分子带有量化的logBB数值（共1058种化合物），而完整数据集则带有分类形式的血脑屏障通透性标签（BBB+或BBB-，总计7807种化合物）。该数据集可在https://github.com/theochem/B3DB免费获取。我们还提供了这些分子的部分理化性质，通过分析这些性质，能够阐明BBB+与BBB-化合物之间的理化异同。