Artificial molecular machines are able to produce and exploit precise nanoscale actuations in response to chemical or physical triggers. Recent scientific efforts have been devoted to the integration, orientation, and interfacing of large assemblies of molecular machines in order to harness their collective actuations at larger length scale and up to the generation of macroscopic motions. Making use of such "hierarchical mechanics" represents a fundamentally new approach for the conception of stimuli-responsive materials. Furthermore, because some molecular machines can function as molecular motors-which are capable of cycling a unidirectional motion out of thermodynamic equilibrium and progressively increasing the work delivered to their environment-one can expect unique opportunities to design new kinds of mechanically active materials and devices capable of autonomous behavior when supplied by an external source of energy. Recently reported achievements are summarized, including the integration of molecular machines at surfaces and interfaces, in 3D self-assembled materials, as well as in liquid crystals and polymer materials. Their detailed functioning principles as well as their functional properties are discussed along with their potential applications in various domains such as sensing, drug delivery, electronics, optics, plasmonics, and mechanics.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/adma.201906036 | DOI Listing |
J Chem Theory Comput
January 2025
State Key Laboratory of Physical Chemistry of Solid Surfaces and Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China.
Molecular docking is a crucial technique for elucidating protein-ligand interactions. Machine learning-based docking methods offer promising advantages over traditional approaches, with significant potential for further development. However, many current machine learning-based methods face challenges in ensuring the physical plausibility of generated docking poses.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Chemical Engineering, National Taiwan University, No. 1, Section 4, Roosevelt Road, Taipei 10617, Taiwan.
Accurately predicting activation energies is crucial for understanding chemical reactions and modeling complex reaction systems. However, the high computational cost of quantum chemistry methods often limits the feasibility of large-scale studies, leading to a scarcity of high-quality activation energy data. In this work, we explore and compare three innovative approaches (transfer learning, delta learning, and feature engineering) to enhance the accuracy of activation energy predictions using graph neural networks, specifically focusing on methods that incorporate low-cost, low-level computational data.
View Article and Find Full Text PDFExpert Opin Drug Discov
January 2025
Department of Radiology, Molecular Imaging Innovations Institute (MI3), Weill Cornell Medicine, New York, NY, USA.
Introduction: Technological advancements in virtual screening (VS) have rapidly accelerated its application in drug discovery, as reflected by the exponential growth in VS-related publications. However, a significant gap remains between the volume of computational predictions and their experimental validation. This discrepancy has led to a rise in the number of unverified 'claimed' hits which impedes the drug discovery efforts.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFSci Rep
January 2025
The First Affiliated Hospital of Zhengzhou University, No.1 Jianshe Road, Zhengzhou, 450052, Henan, China.
Netrin-1 (NTN1) is a laminin-related secreted protein involved in axon guidance and cell migration. Previous research has established a significant connection between NTN1 and nervous system development. In recent years, mounting evidence indicates that NTN1 also plays a crucial role in tumorigenesis and tumor progression.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!