An Interpretable, Thermodynamics-Based Deep Learning Framework for Predicting and Optimizing Drug Membrane Permeability

Cellular
membranes serve as selective barriers, and membrane permeability
is crucial for drug pharmacokinetics. While in vitro and in vivo methods
exist, predicting and designing membrane permeability remains challenging.
We developed a thermodynamics-based deep learning framework to analyze
the structure-permeability relationship, based on the concept that
interactions between membrane lipids and small molecules influence
permeability. We determined the membrane penetration thermodynamics
of 8,239 compounds using coarse-grained molecular dynamics simulations
and created interpretable graph neural network models to predict and
design drug membrane permeability. As a proof-of-concept, we designed
a novel nasal-administered melatonin analog, MT-A2, optimized for
permeability. Compared with melatonin, MT-A2 showed superior nasal
absorption, prolonged brain retention, and enhanced sleep efficacy.
Our results provide a promising approach for predicting and designing
membrane permeability, aiding in the development of drugs with better
pharmacokinetics.