Using a Graph Convolutional Neural Network Model to Identify Bile Salt Export Pump Inhibitors

The bile salt export pump (BSEP) is a key transporter involved in the efflux of bile salts from hepatocytes to bile canaliculi. Inhibition of BSEP leads to the accumulation of bile salts within the hepatocytes, leading to possible cholestasis and drug-induced liver injury. Screening for and identification of chemicals that inhibit this transporter aid in understanding the safety liabilities of these chemicals. Moreover, computational approaches to identify BSEP inhibitors provide an alternative to the more resource-intensive, gold standard experimental approaches. Here, we used publicly available data to develop predictive machine learning models for the identification of potential BSEP inhibitors. Specifically, we analyzed the utility of a graph convolutional neural network (GCNN)-based approach in combination with multitask learning to identify BSEP inhibitors. Our analyses showed that the developed GCNN model performed better than the variable-nearest neighbor and Bayesian machine learning approaches, with a cross-validation receiver operating characteristic area under the curve of 0.86. In addition, we compared GCNN-based single-task and multitask models and evaluated their utility in addressing data limitation challenges commonly observed in bioactivity modeling. We found that multitask models performed better than single-task models and can be utilized to identify active molecules for targets with limited data availability. Overall, our developed multitask GCNN-based BSEP model provides a useful tool for prioritizing hits during early drug discovery and in risk assessment of chemicals.胆汁酸盐输出泵（bile salt export pump, BSEP）是介导胆汁酸盐从肝细胞向胆小管外排的关键转运蛋白。抑制BSEP会导致肝细胞内胆汁酸盐蓄积，进而可能引发胆汁淤积与药物性肝损伤。筛选并鉴定可抑制该转运蛋白的化学物质，有助于评估这类物质的安全性风险。此外，借助计算方法识别BSEP抑制剂，可替代资源消耗更高的金标准实验方法。本研究利用公开数据集构建了用于识别潜在BSEP抑制剂的预测机器学习模型。具体而言，我们分析了结合多任务学习的图卷积神经网络（graph convolutional neural network, GCNN）方法在BSEP抑制剂识别中的应用效果。分析结果显示，所构建的GCNN模型性能优于可变最近邻法与贝叶斯机器学习方法，其交叉验证受试者工作特征曲线下面积可达0.86。此外，我们还对比了基于GCNN的单任务与多任务模型，并评估了它们在解决生物活性建模中常见的数据局限性问题时的实用性。研究发现，多任务模型的表现优于单任务模型，可用于识别数据量有限的靶点的活性分子。综上，本研究开发的基于多任务GCNN的BSEP预测模型，可为早期药物发现阶段的命中化合物优先级筛选以及化学物质风险评估提供实用工具。