Pharmacophore an International Research Journal
Achieving adequate brain exposure remains a central challenge in central nervous system drug discovery. Passive permeability descriptors alone cannot capture the dominant influence of active efflux transporters such as P-glycoprotein on net brain penetration. Existing computational models for blood–brain barrier permeability often emphasize structural or physicochemical correlates without explicitly representing transporter liability. As a result, they may predict whether a compound is likely to penetrate the brain but provide limited mechanistic guidance for medicinal chemistry decisions. This manuscript proposes an attention-based deep learning framework that combines molecular graph representations with predicted transporter liability and CNS-specific molecular descriptors. The goal is to support BBB permeability prediction while highlighting molecular features that could drive poor brain exposure. A graph attention network is used to encode molecular structure, while additional input channels represent P-glycoprotein substrate likelihood and CNS multiparameter optimization descriptors. A molecular-level attention mechanism then weights atom-level and auxiliary feature contributions before a conceptual logBB prediction is generated. Conceptually, the proposed model would be expected to distinguish CNS-penetrant from poorly penetrant compounds by combining passive permeability, transporter liability, and structural context. Its attention maps could identify substructures associated with efflux recognition or favorable brain exposure within chemically related series. This transporter-aware attention framework could support early CNS lead prioritization, guide structural modifications intended to reduce efflux liability, and reduce reliance on late-stage in vivo brain exposure testing. Its value would depend on rigorous validation, transparent feature handling, and prospective use in medicinal chemistry workflows.
