DMInet: An Accurate and Highly Flexible Deep Learning Framework for Drug Membrane Interaction with Membrane Selectivity

TL;DR

DMInet is a deep learning framework that predicts drug-membrane interactions and selectivity using molecular simulation data, enabling faster drug discovery and adaptable to other membrane-related problems.

Contribution

Introduces DMInet, a novel flexible deep learning model leveraging molecular simulation data for predicting drug-membrane interactions and selectivity.

Findings

Accurately predicts potential of mean force across membranes.

Enables high throughput screening in drug discovery.

Flexible architecture applicable to various membrane problems.

Abstract

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.