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。正因如此，血脑屏障与中枢神经系统疾病的发生发展及治疗密切相关，因此预测物质能否穿越血脑屏障，是中枢神经系统药物先导化合物发现过程中的关键任务。机器学习（machine learning, ML）是预测血脑屏障通透性的极具潜力的策略，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，本研究构建了大型基准数据集B3DB，该数据集整合自50项已发表的研究资源，并依据实验不确定性进行分类。B3DB中包含带数值化logBB值的分子子集（共1058个化合物），完整数据集则附带分类形式的血脑屏障通透性标签（BBB+或BBB-，总计7807条数据）。该数据集可通过https://github.com/theochem/B3DB免费获取。本工作同时提供了部分分子的理化性质，通过对这些性质进行分析，可揭示BBB+与BBB-化合物之间的理化异同。