  Drug membrane interaction is a very significant bioprocess to consider in
drug discovery. Here, we propose a novel deep learning framework coined DMInet
to study drug-membrane interactions that leverages large-scale Martini
coarse-grained molecular simulations of permeation of drug-like molecules
across six different lipid membranes. The network of DMInet receives three
inputs, viz, the drug-like molecule, membrane type, and spatial distance across
membrane thickness, and predicts the potential of mean force with structural
resolution across the lipid membrane and membrane selectivity. Inheriting from
coarse-grained Martini representation of organic molecules and combined with
deep learning, DMInet has the potential for more accelerated high throughput
screening in drug discovery across a much larger chemical space than that can
be explored by physics-based simulations alone. Moreover, DMInet is highly
flexible in its nature and holds the possibilities for other properties
prediction without significant changes in the architecture. Last but not least,
the architecture of DMInet is general and can be applied to other membrane
problems involving permeation and selection.
