Issue 35, 2020

A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes

Abstract

Study of the permeability of small organic molecules across lipid membranes plays a significant role in designing potential drugs in the field of drug discovery. Approaches to design promising drug molecules have gone through many stages, from experiment-based trail-and-error approaches, to the well-established avenue of the quantitative structure–activity relationship, and currently to the stage guided by machine learning (ML) and artificial intelligence techniques. In this work, we present a study of the permeability of small drug-like molecules across lipid membranes by two types of ML models, namely the least absolute shrinkage and selection operator (LASSO) and deep neural network (DNN) models. Molecular descriptors and fingerprints are used for featurization of organic molecules. Using molecular descriptors, the LASSO model uncovers that the electro-topological, electrostatic, polarizability, and hydrophobicity/hydrophilicity properties are the most important physical properties to determine the membrane permeability of small drug-like molecules. Additionally, with molecular fingerprints, the LASSO model suggests that certain chemical substructures can significantly affect the permeability of organic molecules, which closely connects to the identified main physical properties. Moreover, the DNN model using molecular fingerprints can help develop a more accurate mapping between molecular structures and their membrane permeability than LASSO models. Our results provide deep understanding of drug–membrane interactions and useful guidance for the inverse molecular design of drug-like molecules. Last but not least, while the current focus is on the permeability of drug-like molecules, the methodology of this work is general and can be applied for other complex physical chemistry problems to gain molecular insights.

Graphical abstract: A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes

Article information

Article type
Paper
Submitted
16 ጁን 2020
Accepted
27 ጁላይ 2020
First published
27 ጁላይ 2020

Phys. Chem. Chem. Phys., 2020,22, 19687-19696

Author version available

A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes

G. Chen, Z. Shen and Y. Li, Phys. Chem. Chem. Phys., 2020, 22, 19687 DOI: 10.1039/D0CP03243C

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements