The blood-brain barrier (BBB) selectively regulates the passage of chemical compounds into and out of the central nervous system (CNS). As such, understanding the permeability of drug molecules through the BBB is key to treating neurological diseases and evaluating the response of the CNS to medical treatments. Within the last two decades, a diverse portfolio of machine learning (ML) models have been regularly utilized as a tool to predict, and, to a much lesser extent, understand, several functional properties of medicinal drugs, including their propensity to pass through the BBB. However, the most numerically accurate models to date lack in transparency, as they typically rely on complex blends of different descriptors (or features or fingerprints), many of which are not necessarily interpretable in a straightforward fashion. In fact, the "black-box" nature of these models has prevented us from pinpointing any specific design rule to craft the next generation of pharmaceuticals that need to pass (or not) through the BBB. In this work, we have developed a ML model that leverages an uncomplicated, transparent set of descriptors to predict the permeability of drug molecules through the BBB. In addition to its simplicity, our model achieves comparable results in terms of accuracy compared to state-of-the-art models. Moreover, we use a naive Bayes model as an analytical tool to provide further insights into the structure-function relation that underpins the capacity of a given drug molecule to pass through the BBB. Although our results are computational rather than experimental, we have identified several molecular fragments and functional groups that may significantly impact a drug's likelihood of permeating the BBB. This work provides a unique angle to the BBB problem and lays the foundations for future work aimed at leveraging additional transparent descriptors, potentially obtained via bespoke molecular dynamics simulations.

中文翻译：

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透明的机器学习模型，用于了解药物通过血脑屏障的渗透性。


血脑屏障 （BBB） 选择性地调节化合物进出中枢神经系统 （CNS） 的通道。因此，通过 BBB 了解药物分子的通透性是治疗神经系统疾病和评估 CNS 对药物治疗的反应的关键。在过去的二十年里，各种各样的机器学习 （ML） 模型组合经常被用作预测和在较小程度上理解药物的多种功能特性的工具，包括它们通过 BBB 的倾向。然而，迄今为止数值最准确的模型缺乏透明度，因为它们通常依赖于不同描述符（或特征或指纹）的复杂混合，其中许多不一定能以简单的方式解释。事实上，这些模型的“黑盒”性质使我们无法确定任何特定的设计规则来制作需要（或不通过）BBB 的下一代药物。在这项工作中，我们开发了一个 ML 模型，该模型利用一组简单、透明的描述符来预测药物分子通过 BBB 的渗透性。除了简单性之外，与最先进的模型相比，我们的模型在准确性方面取得了相当的结果。此外，我们使用朴素贝叶斯模型作为分析工具，以进一步了解支撑给定药物分子通过 BBB 的能力的结构-功能关系。尽管我们的结果是计算而不是实验，但我们已经确定了几个可能显着影响药物渗透 BBB 可能性的分子片段和官能团。 这项工作为 BBB 问题提供了一个独特的角度，并为未来的工作奠定了基础，这些工作旨在利用额外的透明描述符，这些描述符可能通过定制的分子动力学模拟获得。