We hereby introduce the atomic degree of interaction (DOI), a new concept rooted in the electron density-based independent gradient model (IGM). Capturing any manifestation of electron density sharing around an atom, including covalent and non-covalent situations, this index reflects the attachment strength of an atom to its molecular neighbourhood. It is shown to be very sensitive to the local chemical environment of the atom. No significant correlation could be found between the atomic DOI and various other atomic properties, making this index a specific source of information. However, examining the simple H + H reacting system, a strong connection has been established between this electron density-based index and the scalar reaction path curvature, the cornerstone of the benchmark unified reaction valley approach (URVA). We observe that reaction path curvature peaks appear when atoms experience an acceleration phase of electron density sharing during the reaction, detected by peaks of the DOI second derivative either in the forward or reverse direction. This new IGM-DOI tool is only in its early stages, but it opens the way to an atomic-level interpretation of reaction phases. More generally, the IGM-DOI tool may also serve as an atomic probe of electronic structure changes of a molecule under the influence of physicochemical perturbations.
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http://dx.doi.org/10.1039/d2cp02839e | DOI Listing |
J Chem Phys
January 2025
Department of Computer Science, Stanford University, Stanford, California 94305, USA.
Atomic-level simulations are widely used to study biomolecules and their dynamics. A common goal in such studies is to compare simulations of a molecular system under several conditions-for example, with various mutations or bound ligands-in order to identify differences between the molecular conformations adopted under these conditions. However, the large amount of data produced by simulations of ever larger and more complex systems often renders it difficult to identify the structural features that are relevant to a particular biochemical phenomenon.
View Article and Find Full Text PDFJ Chem Inf Model
January 2025
Department of Computer Science and Engineering, and Key Laboratory of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering, Shanghai Jiao Tong University, 800 Dongchuan Road, Shanghai 200240, China.
Despite remarkable advancements in the organic synthesis field facilitated by the use of machine learning (ML) techniques, the prediction of reaction outcomes, including yield estimation, catalyst optimization, and mechanism identification, continues to pose a significant challenge. This challenge arises primarily from the lack of appropriate descriptors capable of retaining crucial molecular information for accurate prediction while also ensuring computational efficiency. This study presents a successful application of ML for predicting the performance of Ir-catalyzed allylic substitution reactions.
View Article and Find Full Text PDFNat Commun
November 2024
Division of Biology and Chemistry, Paul Scherrer Institut, Villigen, Switzerland.
Time-resolved serial crystallography at X-ray Free Electron Lasers offers the opportunity to observe ultrafast photochemical reactions at the atomic level. The technique has yielded exciting molecular insights into various biological processes including light sensing and photochemical energy conversion. However, to achieve sufficient levels of activation within an optically dense crystal, high laser power densities are often used, which has led to an ongoing debate to which extent photodamage may compromise interpretation of the results.
View Article and Find Full Text PDFJ Chem Inf Model
December 2024
College of Metallurgy and Energy Engineering, Kunming University of Science and Technology, Kunming 650031, China.
Accurately identifying sites of metabolism (SoM) mediated by cytochrome P450 (CYP) enzymes, which are responsible for drug metabolism in the body, is critical in the early stage of drug discovery and development. Current computational methods for CYP-mediated SoM prediction face several challenges, including limitations to traditional machine learning models at the atomic level, heavy reliance on complex feature engineering, and the lack of interpretability relevant to medicinal chemistry. Here, we propose GraphCySoM, a novel molecule-level modeling approach based on graph neural networks, utilizing lightweight features and interpretable annotations on substructures, to effectively and interpretably predict CYP-mediated SoM.
View Article and Find Full Text PDFBioinformatics
November 2024
Machine Intellection Department, Institute for Infocomm Research, Agency for Science, Technology and Research (A*STAR), Singapore 138632, Singapore.
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