A-iMPO: A Design-Time Prediction Toolbox for Blood Brain Barrier Permeation Using an Integrated and Interpretable Multiparameter Optimization Framework

Abstract

Design time prioritization of central nervous system (CNS) drug candidates remains a challenge due to the restrictive nature of the blood–brain barrier (BBB) governing brain exposure. Although various multiparameter optimization (MPO) strategies have guided CNS medicinal chemistry for over a decade, existing frameworks rely heavily on heuristic cutoffs and offer limited interpretability across chemically diverse scaffolds. Here, we introduce a next generation CNS-MPO framework─Aragen-iMPO, (A-iMPO)─which was developed using 5,129 curated compounds through an integrated workflow combining explainable machine learning, rigorous descriptor selection, and low-dimensional discriminant mapping. This yielded six chemically intuitive features capturing polarity, ionization, size, rigidity, and electronic distribution. The resulting score provides a transparent discriminant function enabling direct compound prioritization through a simple threshold rule. Across internal and external validation sets, A-iMPO outperformed established CNS-focused scoring methods while maintaining mechanistic interpretability, providing a practical and design ready tool for CNS drug discovery.