Enumerating chemical compounds with given structural properties plays an important role in structure elucidation, with applications such as drug design. We focus on the problem of enumerating tree-like chemical graphs specified by upper and lower bounds on feature vectors, where chemical graphs represent compounds, and a feature vector characterizes frequencies of finite paths in a graph. Building on the branch-and-bound algorithm proposed in earlier work, we propose a new bounding procedure, called Resource Cut, to speed up the enumeration process. Tree-like chemical graphs are modeled as vertex-colored trees, colors representing chemical elements. The algorithm is based on a scheme of generating each unique colored tree with a specified number n of vertices. A colored tree is constructed by repeatedly appending vertices. Given a set R of n colored vertices, we found that the algorithm often constructs trees that cannot be extended to a unique representation of a colored tree no matter how the remaining unused colored vertices in the set R are appended. We derive a mathematical condition to detect and discard such trees. Experimental results show that Resource Cut significantly reduces the search space. We have been able to obtain exact numbers of chemical graphs with up to 17 vertices excluding hydrogen atoms.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1109/TCBB.2018.2832061 | DOI Listing |
BMC 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 PDFJ Mol Graph Model
January 2025
Amity Institute of Biotechnology, Amity University Uttar Pradesh, Lucknow Campus, Gomtinagar Extension, Lucknow, 226028, India; Research Cell, Amity University Uttar Pradesh, Lucknow Campus, India. Electronic address:
The Acinetobacter baumannii is a member of the "ESKAPE" bacteria responsible for many serious multidrug-resistant (MDR) illnesses. This bacteria swiftly adapts to environmental cues leading to the emergence of multidrug-resistant variants, particularly in hospital/medical settings. In this work, we have demonstrated the outer membrane protein 33-36 (Omp33-36) porin as a potential therapeutic target in A.
View Article and Find Full Text PDFJ Mol Graph Model
January 2025
"VINČA" Institute of Nuclear Sciences - National Institute of the Republic of Serbia, University of Belgrade, 11001, Belgrade, Serbia.
Technetium-99m plays a pivotal role in nuclear medicine, offering unique IMAGING capabilities due to its favorable physical and chemical properties. This study investigates the redox behavior and electronic structures of three representative Tc(V) oxo complexes, [TcO(HMPAO)], [TcO(Bicisate)], and [TcO(DMSA)], using computational techniques. Employing relativistic density functional theory with the Zero-Order Regular Approximation (ZORA), we analyze singlet-triplet energy gaps, Gibbs free energy changes, and redox potentials in neutral and acidic environments.
View Article and Find Full Text PDFMol Divers
January 2025
Key Laboratory for Macromolecular Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded "message passing" structures at the atomic scale and spatial feature information "encoder-decoder" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values.
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 PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!