Permeability Prediction in chemistry AI refers to machine learning models that predict a molecule's ability to cross biological membranes, particularly the intestinal epithelium (measured via Caco-2 cell assays) and the blood-brain barrier (BBB), from molecular structure. Membrane permeability directly determines oral bioavailability and CNS drug access, making it one of the most critical ADMET properties predicted by computational methods.

Why Permeability Prediction Matters in AI/ML: Permeability is a primary determinant of oral drug bioavailability—even potent compounds fail as drugs if they cannot cross intestinal membranes—and AI prediction enables early filtering of impermeable candidates before expensive in vitro Caco-2 or PAMPA assays.

• Caco-2 permeability models — ML models predict apparent permeability (Papp) through Caco-2 cell monolayers, the gold standard in vitro assay for intestinal absorption; models classify compounds as high/low permeability or predict continuous log Papp values • PAMPA prediction — Parallel Artificial Membrane Permeability Assay (PAMPA) measures passive transcellular permeability without active transport; ML models for PAMPA are simpler since they only need to capture passive diffusion, which correlates strongly with lipophilicity and molecular size • BBB penetration — Blood-brain barrier permeability models predict whether compounds can access the central nervous system: critical for CNS drug design (need penetration) and peripheral drug design (should avoid penetration to prevent CNS side effects) • Lipinski's Rule of Five — The classical heuristic: MW < 500, logP < 5, HBD < 5, HBA < 10 predicts oral bioavailability; ML models significantly outperform this rule by capturing nonlinear relationships and molecular shape effects • Active transport vs. passive diffusion — Permeability involves both passive transcellular/paracellular diffusion and active transport (efflux pumps like P-gp, influx transporters); comprehensive models must account for both mechanisms

Permeability prediction is a cornerstone of AI-driven ADMET profiling, enabling rapid computational screening of membrane transport properties that determine whether drug candidates can reach their biological targets, reducing the reliance on expensive and time-consuming in vitro cell-based assays while accelerating the identification of orally bioavailable drug molecules.