Light-driven molecular rotary motors perform chirality-controlled unidirectional rotations fueled by light and heat. This unique function renders them appealing for the construction of dynamic molecular systems, actuating materials, and molecular machines. Achieving a combination of high photoefficiency, visible-light responsiveness, synthetic accessibility, and easy tuning of dynamic properties within a single scaffold is critical for these applications but remains a longstanding challenge. Herein, a series of highly photoefficient visible-light-responsive molecular motors (MMs), featuring various rotary speeds, was obtained by a convenient one-step formylation of their parent motors. This strategy greatly improves all aspects of the performance of MMs-red-shifted wavelengths of excitation, high photoisomerization quantum yields, and high photostationary state distributions of isomers-beyond the state-of-the-art light-responsive MM systems. The development of this late-stage functionalization strategy of MMs opens avenues for the construction of high-performance molecular machines and devices for applications in materials science and biological systems, representing a major advance in the synthetic toolbox of molecular machines.
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http://dx.doi.org/10.1126/sciadv.adr9326 | DOI Listing |
Front Immunol
March 2025
Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.
Background: Neutrophil extracellular traps (NETs) play pivotal roles in various pathological processes. The formation of NETs is impaired in acute myeloid leukemia (AML), which can result in immunodeficiency and increased susceptibility to infection.
Methods: The gene set variation analysis (GSVA) algorithm was employed for the calculation of NET score, while the consensus clustering algorithm was utilized to identify molecular subtypes.
Iran J Pharm Res
November 2024
Department of Pharmaceutics, School of Pharmacy, Guilan University of Medical Sciences, Rasht, Iran.
The inefficiency of some medications to cross the blood-brain barrier (BBB) is often attributed to their poor physicochemical or pharmacokinetic properties. Recent studies have demonstrated promising outcomes using machine learning algorithms to predict drug permeability across the BBB. In light of these findings, our study was conducted to explore the potential of machine learning in predicting the permeability of drugs across the BBB.
View Article and Find Full Text PDFFront Bioinform
February 2025
School of Medicine, Aristotle University of Thessaloniki, Thessaloniki, Greece.
Machine learning and genomic medicine are the mainstays of research in delivering personalized healthcare services for disease diagnosis, risk stratification, tailored treatment, and prediction of adverse effects. However, potential prediction errors in healthcare services can have life-threatening impact, raising reasonable skepticism about whether these applications have practical benefit in clinical settings. Conformal prediction offers a versatile framework for addressing these concerns by quantifying the uncertainty of predictive models.
View Article and Find Full Text PDFJ Chem Theory Comput
March 2025
Theoretical Molecular Science Laboratory, RIKEN Cluster for Pioneering Research, Wako, Saitama 351-0198, Japan.
Machine learning (ML) methods have emerged as an efficient surrogate for high-level electronic structure theory, offering precision and computational efficiency. However, the vast conformational and chemical space remains challenging when constructing a general force field. Training data sets typically cover only a limited region of this space, resulting in poor extrapolation performance.
View Article and Find Full Text PDFJ Cheminform
March 2025
Machine Learning Research, Pfizer Research and Development, Berlin, Germany.
Transformer-based, template-free SMILES-to-SMILES translation models for reaction prediction and single-step retrosynthesis are of interest to computer-aided synthesis planning systems, as they offer state-of-the-art accuracy. However, their slow inference speed limits their practical utility in such applications. To address this challenge, we propose speculative decoding with a simple chemically specific drafting strategy and apply it to the Molecular Transformer, an encoder-decoder transformer for conditional SMILES generation.
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