Breaking the Barriers: Machine-Learning-Based c‑RASAR Approach for Accurate Blood–Brain Barrier Permeability Prediction

The intricate nature of the blood–brain barrier (BBB) poses a significant challenge in predicting drug permeability, which is crucial for assessing central nervous system (CNS) drug efficacy and safety. This research utilizes an innovative approach, the classification read-across structure–activity relationship (c-RASAR) framework, that leverages machine learning (ML) to enhance the accuracy of BBB permeability predictions. The c-RASAR framework seamlessly integrates principles from both read-across and QSAR methodologies, underscoring the need to consider similarity-related aspects during the development of the c-RASAR model. It is crucial to note that the primary goal of this research is not to introduce yet another model for predicting BBB permeability but rather to showcase the refinement in predicting the BBB permeability of organic compounds through the introduction of a c-RASAR approach. This groundbreaking methodology aims to elevate the accuracy of assessing neuropharmacological implications and streamline the process of drug development. In this study, an ML-based c-RASAR linear discriminant analysis (LDA) model was developed using a dataset of 7807 compounds, encompassing both BBB-permeable and -nonpermeable substances sourced from the B3DB database (freely accessible from https://github.com/theochem/B3DB), for predicting BBB permeability in lead discovery for CNS drugs. The model’s predictive capability was then validated using three external sets: one containing 276,518 natural products (NPs) from the LOTUS database (accessible from https://lotus.naturalproducts.net/download) for data gap filling, another comprising 13,002 drug-like/drug compounds from the DrugBank database (available from https://go.drugbank.com/), and a third set of 56 FDA-approved drugs to assess the model’s reliability. Further diversifying the predictive arsenal, various other ML-based c-RASAR models were also developed for comparison purposes. The proposed c-RASAR framework emerged as a powerful tool for predicting BBB permeability. This research not only advances the understanding of molecular determinants influencing CNS drug permeability but also provides a versatile computational platform for the rapid assessment of diverse compounds, facilitating informed decision-making in drug development and design.

血脑屏障（blood–brain barrier, BBB）的复杂特性为药物透过性预测带来了重大挑战，而该指标对于评估中枢神经系统（central nervous system, CNS）药物的疗效与安全性至关重要。本研究采用了一项创新方法——分类交叉参照构效关系（classification read-across structure–activity relationship, c-RASAR）框架，借助机器学习（machine learning, ML）提升血脑屏障透过性预测的准确性。该c-RASAR框架无缝整合了交叉参照与定量构效关系（quantitative structure–activity relationship, QSAR）方法的原理，凸显了在开发c-RASAR模型过程中考量相似性相关因素的必要性。需特别说明的是，本研究的核心目标并非再新增一款血脑屏障透过性预测模型，而是通过引入c-RASAR方法，优化有机化合物血脑屏障透过性的预测效果。这一开创性方法旨在提升神经药理学相关影响评估的准确性，并简化药物开发流程。本研究依托从B3DB数据库（可从https://github.com/theochem/B3DB免费获取）获取的包含7807种化合物的数据集——涵盖血脑屏障透过型与非透过型物质，开发了基于机器学习的c-RASAR线性判别分析（linear discriminant analysis, LDA）模型，用于预测中枢神经系统药物先导发现阶段的血脑屏障透过性。随后，研究团队通过三个外部数据集对该模型的预测能力进行验证：其一为来自LOTUS数据库（可从https://lotus.naturalproducts.net/download获取）的276518种天然产物（natural products, NPs），用于填补数据空白；其二为来自DrugBank数据库（可从https://go.drugbank.com/获取）的13002种类药物/药物化合物；其三为56种FDA批准药物，用于评估模型的可靠性。为进一步丰富预测对比的工具类型，研究团队还开发了多款其他基于机器学习的c-RASAR模型用于对照。所提出的c-RASAR框架成为了血脑屏障透过性预测的强有力工具。本研究不仅加深了对影响中枢神经系统药物透过性的分子决定因素的理解，还提供了一个通用计算平台，可快速评估各类化合物，助力药物开发与设计中的科学决策。