DataSheet1_A Machine Learning Model to Predict Drug Transfer Across the Human Placenta Barrier.PDF

The development of computational models for assessing the transfer of chemicals across the placental membrane would be of the utmost importance in drug discovery campaigns, in order to develop safe therapeutic options. We have developed a low-dimensional machine learning model capable of classifying compounds according to whether they can cross or not the placental barrier. To this aim, we compiled a database of 248 compounds with experimental information about their placental transfer, characterizing each compound with a set of ∼5.4 thousand descriptors, including physicochemical properties and structural features. We evaluated different machine learning classifiers and implemented a genetic algorithm, in a five cross validation scheme, to perform feature selection. The optimization was guided towards models displaying a low number of false positives (molecules that actually cross the placental barrier, but are predicted as not crossing it). A Linear Discriminant Analysis model trained with only four structural features resulted to be robust for this task, exhibiting only one false positive case across all testing folds. This model is expected to be useful in predicting placental drug transfer during pregnancy, and thus could be used as a filter for chemical libraries in virtual screening campaigns.

在药物发现过程中，针对评估化学物质穿越胎盘膜转移的计算机模型的开发显得极为重要，旨在开发安全的治疗选择。本研究开发了一个低维度的机器学习模型，该模型能够根据化合物是否能够穿越胎盘屏障对其进行分类。为此，我们编纂了一个包含248种化合物及其胎盘转移实验信息的数据库，每个化合物均以约5.4千个描述符进行表征，这些描述符包括物理化学性质和结构特征。我们对不同的机器学习分类器进行了评估，并实施了一种遗传算法，采用五折交叉验证方案进行特征选择。模型优化旨在减少误报数量（即实际上能够穿越胎盘屏障但预测结果为不能穿越的分子）。经过训练，仅使用四个结构特征的线性判别分析模型在所有测试折中仅出现一个误报案例，表现出极高的鲁棒性。该模型预计在预测孕期胎盘药物转移方面具有实用性，因此可被用作虚拟筛选活动中化学库的过滤器。