skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


Title: A machine-learning-assisted study of the permeability of small drug-like molecules across lipid membranes
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.  more » « less
Award ID(s):
1755779
PAR ID:
10211812
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
Physical Chemistry Chemical Physics
Volume:
22
Issue:
35
ISSN:
1463-9076
Page Range / eLocation ID:
19687 to 19696
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; ; ; ; ; ; (, Chemical Physics Reviews)
    Polymeric membranes have become essential for energy-efficient gas separations such as natural gas sweetening, hydrogen separation, and carbon dioxide capture. Polymeric membranes face challenges like permeability-selectivity tradeoffs, plasticization, and physical aging, limiting their broader applicability. Machine learning (ML) techniques are increasingly used to address these challenges. This review covers current ML applications in polymeric gas separation membrane design, focusing on three key components: polymer data, representation methods, and ML algorithms. Exploring diverse polymer datasets related to gas separation, encompassing experimental, computational, and synthetic data, forms the foundation of ML applications. Various polymer representation methods are discussed, ranging from traditional descriptors and fingerprints to deep learning-based embeddings. Furthermore, we examine diverse ML algorithms applied to gas separation polymers. It provides insights into fundamental concepts such as supervised and unsupervised learning, emphasizing their applications in the context of polymer membranes. The review also extends to advanced ML techniques, including data-centric and model-centric methods, aimed at addressing challenges unique to polymer membranes, focusing on accurate screening and inverse design. 
    more » « less
  2. ; ; ; ; ; ; ; ; ; (, Advanced Materials Technologies)
    Abstract Cell membranes are fundamental for cellular function as they protect the cell and control passage in and out of the cell. Despite their clear significance, cell membranes are often difficult to study, due to their complexity and the lack of available technologies to interface with them and transduce their functions. Overcoming this complexity by developing simple, reductionist models can facilitate their study. Indeed, lipid layers represent a simplified yet representative model for a cell membrane. Lipid layers are highly insulating, a property that is directly affected by changes in lipid packing or membrane fluidity. Such physical changes in the membrane models can be characterized by coupling them with an electronic transducer. Herein, a lipid monolayer that is stabilized between two immiscible solvents is integrated with an organic electrochemical transistor, which is capable of operating in a biphasic solvent mixture. The platform is used to evaluate how lidocaine, a widely used anesthetic the working mechanism of which is still a matter of debate, interacts with the cell membrane. The present study provides evidence that the anesthetic directly interacts with the lipids in the membrane, affecting their packing and therefore the monolayer permeability. The proposed platform provides an elegant solution for studying compound–membrane interactions. 
    more » « less
  3. ; ; ; (, Science Advances)
    Cell-free systems have enabled the development of genetically encoded biosensors to detect a range of environmental and biological targets. Encapsulation of these systems in synthetic membranes to form artificial cells can reintroduce features of the cellular membrane, including molecular containment and selective permeability, to modulate cell-free sensing capabilities. Here, we demonstrate robust and tunable performance of a transcriptionally regulated, cell-free riboswitch encapsulated in lipid membranes, allowing the detection of fluoride, an environmentally important molecule. Sensor response can be tuned by varying membrane composition, and encapsulation protects from sensor degradation, facilitating detection in real-world samples. These sensors can detect fluoride using two types of genetically encoded outputs, enabling detection of fluoride at the Environmental Protection Agency maximum contaminant level of 0.2 millimolars. This work demonstrates the capacity of bilayer membranes to confer tunable permeability to encapsulated, genetically encoded sensors and establishes the feasibility of artificial cell platforms to detect environmentally relevant small molecules. 
    more » « less
  4. ; ; (, Proceedings of the National Academy of Sciences)
    Lipid membranes are complex quasi–two-dimensional fluids, whose importance in biology and unique physical/materials properties have made them a major target for biophysical research. Recent single-molecule tracking experiments in membranes have caused some controversy, calling the venerable Saffman–Delbrück model into question and suggesting that, perhaps, current understanding of membrane hydrodynamics is imperfect. However, single-molecule tracking is not well suited to resolving the details of hydrodynamic flows; observations involving correlations between multiple molecules are superior for this purpose. Here dual-color molecular tracking with submillisecond time resolution and submicron spatial resolution is employed to reveal correlations in the Brownian motion of pairs of fluorescently labeled lipids in membranes. These correlations extend hundreds of nanometers in freely floating bilayers (black lipid membranes) but are severely suppressed in supported lipid bilayers. The measurements are consistent with hydrodynamic predictions based on an extended Saffman–Delbrück theory that explicitly accounts for the two-leaflet bilayer structure of lipid membranes. 
    more » « less
  5. ; ; ; ; (, Nature Communications)
    Abstract Interatomic potentials derived with Machine Learning algorithms such as Deep-Neural Networks (DNNs), achieve the accuracy of high-fidelity quantum mechanical (QM) methods in areas traditionally dominated by empirical force fields and allow performing massive simulations. Most DNN potentials were parametrized for neutral molecules or closed-shell ions due to architectural limitations. In this work, we propose an improved machine learning framework for simulating open-shell anions and cations. We introduce the AIMNet-NSE (Neural Spin Equilibration) architecture, which can predict molecular energies for an arbitrary combination of molecular charge and spin multiplicity with errors of about 2–3 kcal/mol and spin-charges with error errors ~0.01e for small and medium-sized organic molecules, compared to the reference QM simulations. The AIMNet-NSE model allows to fully bypass QM calculations and derive the ionization potential, electron affinity, and conceptual Density Functional Theory quantities like electronegativity, hardness, and condensed Fukui functions. We show that these descriptors, along with learned atomic representations, could be used to model chemical reactivity through an example of regioselectivity in electrophilic aromatic substitution reactions. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email