Transparent Machine Learning Model to Understand Drug Permeability through the Blood-Brain Barrier.

IF 5.6 2区 化学 Q1 CHEMISTRY, MEDICINAL
Hengjian Jia, Gabriele C Sosso
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引用次数: 0

Abstract

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.

了解药物通过血脑屏障渗透性的透明机器学习模型
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,了解药物分子通过 BBB 的渗透性是治疗神经系统疾病和评估中枢神经系统对药物治疗反应的关键。在过去的二十年里,人们经常利用各种机器学习(ML)模型来预测药物的若干功能特性,包括它们通过 BBB 的倾向性,但对药物功能特性的了解程度要低得多。然而,迄今为止最精确的数字模型都缺乏透明度,因为它们通常依赖于不同描述因子(或特征或指纹)的复杂混合,其中许多不一定能以直接的方式进行解释。事实上,这些模型的 "黑箱 "性质使我们无法确定任何具体的设计规则,来设计需要通过(或不需要)BBB的下一代药物。在这项工作中,我们开发了一种 ML 模型,利用一组不复杂、透明的描述符来预测药物分子通过 BBB 的渗透性。除了简单之外,我们的模型在准确性方面也达到了与最先进模型相当的结果。此外,我们还利用天真贝叶斯模型作为分析工具,进一步深入了解了特定药物分子通过 BBB 的能力所依赖的结构-功能关系。虽然我们的研究结果是计算性的而非实验性的,但我们发现了一些分子片段和功能基团,它们可能会对药物渗透 BBB 的可能性产生重大影响。这项工作为解决 BBB 问题提供了一个独特的角度,并为今后的工作奠定了基础,目的是利用更多的透明描述符,这些描述符有可能是通过定制的分子动力学模拟获得的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
9.80
自引率
10.70%
发文量
529
审稿时长
1.4 months
期刊介绍: The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.
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