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.