A new area of applied chemistry called chemical graph theory uses combinatorial techniques to explain the complex interactions between atoms and bonds in chemical systems. This work investigates the use of edge partitions to decipher molecular connection patterns. The main goal is to use topological indices that capture important topological features to create a connection between the thermodynamic properties and structural characteristics of chemical molecules. We specifically examine the complex web of atoms and links that make up the Fe phthalocyanine chemical graph. Moreover, our study demonstrates a relationship between the calculated topological indices and the thermodynamic properties of Fe phthalocyanine (Phthalocyanine Iron (II)). This work offers insight into the thermodynamic consequences of molecule structures. It advances the subject of chemical graph theory, providing a useful perspective for future applications in catalysis and materials science.
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http://dx.doi.org/10.1038/s41598-024-69517-x | DOI Listing |
Innovation (Camb)
January 2025
AIM Center, College of Life Sciences and Technology, Beijing University of Chemical Technology, Institute of Microbiology, Chinese Academy of Sciences, Beijing, China.
Predicting free energy changes (ΔΔG) is essential for enhancing our understanding of protein evolution and plays a pivotal role in protein engineering and pharmaceutical development. While traditional methods offer valuable insights, they are often constrained by computational speed and reliance on biased training datasets. These constraints become particularly evident when aiming for accurate ΔΔG predictions across a diverse array of protein sequences.
View Article and Find Full Text PDFHeliyon
January 2025
College of Since and Art, Department of Mathematics, King Khalid University, Mahayil, Saudi Arabia.
New developments in the field of chemical graph theory have made it easier to comprehend how chemical structures relate to the graphs that underlie them on a more profound level using the ideas of classical graph theory. Chemical graphs can be effectively probed with the help of quantitative structure-property relationship (QSPR) analysis. In order to statistically correlate physical attributes.
View Article and Find Full Text PDFProtein Sci
February 2025
Department of Physics, University of Washington, Seattle, Washington, USA.
Proteins' flexibility is a feature in communicating changes in cell signaling instigated by binding with secondary messengers, such as calcium ions, associated with the coordination of muscle contraction, neurotransmitter release, and gene expression. When binding with the disordered parts of a protein, calcium ions must balance their charge states with the shape of calcium-binding proteins and their versatile pool of partners depending on the circumstances they transmit. Accurately determining the ionic charges of those ions is essential for understanding their role in such processes.
View Article and Find Full Text PDFNMR Biomed
March 2025
Centre for Advanced Imaging, The University of Queensland, St Lucia, Queensland, Australia.
In this work, we introduce spatial and chemical saturation options for artefact reduction in magnetic resonance fingerprinting (MRF) and assess their impact on T and T mapping accuracy. An existing radial MRF pulse sequence was modified to enable spatial and chemical saturation. Phantom experiments were performed to demonstrate flow artefact reduction and evaluate the accuracy of the T and T maps.
View Article and Find Full Text PDFNat Commun
January 2025
Key Laboratory of Quantum Materials and Devices of Ministry of Education, School of Physics, Southeast University, Nanjing, 21189, China.
Directly generating material structures with optimal properties is a long-standing goal in material design. Traditional generative models often struggle to efficiently explore the global chemical space, limiting their utility to localized space. Here, we present a framework named Material Generation with Efficient Global Chemical Space Search (MAGECS) that addresses this challenge by integrating the bird swarm algorithm and supervised graph neural networks, enabling effective navigation of generative models in the immense chemical space towards materials with target properties.
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