Advanced Intelligent Computing Technology and Applications

Table of Contents

1. Frontmatter

2. Cheminformatics

   1. Frontmatter

   2. Topological Analysis of F-Multiplicity Corona Graphs: Zagreb Indices and Applications in Molecular Design

      Yang Yang, Mengjun Wang, Jiangtao Xu, Jinyun Wang

      Abstract

      For graph \(G\), the first Zagreb index \({M}_{1}\left(G\right)\) and second Zagreb index \({M}_{2}\left(G\right)\) are defined as: \({M}_{1}\left(G\right)={\sum }_{u\in V\left(G\right)}{d}_{G}^{2}\left(u\right)\) and \({M}_{2}\left(G\right)={\sum }_{u\upnu \in E\left(G\right)}{d}_{G}\left(u\right){d}_{G}\left(\upnu \right)\), where \({d}_{G}\left(v\right)\) denotes the degree of vertex \(v\) in \(G\). The Hyper-Zagreb index \(HM\left(G\right)\) is defined as: \(HM\left(G\right)={\sum }_{u\nu \in E\left(G\right)}{\left[{d}_{G}\left(u\right)+{d}_{G}\left(\nu \right)\right]}^{2}\). In this paper, we introduce a novel class of F-multiplicity corona graphs and derive explicit formulas for their \({M}_{1}\left(G\right)\), \({M}_{2}\left(G\right)\) and \(HM\left(G\right)\). Additionally, we demonstrate the chemical relevance of these graphs through applications in molecular design, highlighting their potential for modeling complex chemical structures.

   3. Graph-Based Multi-scale Learning for Predicting Mass Spectra from Molecules

      Guoyu Hu, Simeng Huang, Zeyang Zhu, Changbo Ke, Bolei Zhang

      Abstract

      The computational prediction of mass spectra is a vital approach for identifying small-molecule structures. Existing methods have adopted approaches such as rule-based frameworks and deep learning models to predict mass spectra from molecular structures. However, due to the complex structure of molecules and the intricate fragmentation patterns in mass spectra, it is still challenging to effectively utilize the local and global features for modeling molecular fragmentation. The local features of molecules are vital for capturing the fine-grained structural details, while the global features are essential for understanding the complex interactions between substructures. In this work, we propose GraphMS, a novel model that integrates local and global graph features for mass spectral prediction. We first decompose the molecular graph into chemically meaningful substructures for extracting local features. A Transformer module is then applied to capture global features and model long-range dependencies between super nodes. Finally, the local and global features are fused adaptively to create a comprehensive molecular representation for mass spectrum prediction. Experiments on multiple datasets demonstrate that GraphMS outperforms existing methods, achieving higher accuracy and better generalization in mass spectrum prediction.

   4. A Universal Periodicity Injection Module for Crystal Property Prediction

      Yichao Fu, Ke Liu, Shangde Gao, Lai Wei, Te Qiao, Chao Zhang

      Abstract

      Crystals are essential constituents of a wide range of materials, encompassing both advanced technologies and everyday applications. Recently, deep learning-based methods for crystal property prediction have demonstrated remarkable performance, greatly facilitating the discovery of novel materials. However, these approaches typically concentrate on atom-wise interactions and often fail to account for periodicity, a fundamental characteristic of crystals. To address this limitation, we propose a novel plug-and-play component, the Periodicity Injection Module (PIM), which seamlessly incorporates periodicity into existing crystal models. Specifically, the PIM employs crystal-wise attention to ensure that the surroundings of unit cells at periodic distances remain identical, aligning with the definition of crystal periodicity. By capturing interactions among bases at the crystal level, the PIM complements and enhances the modeling of intra-crystal interactions. Extensive experiments on benchmark datasets demonstrate that our PIM significantly improves crystal property prediction.

   5. SM-CBNet: A Speech-Based Parkinson’s Disease Diagnosis Model with SMOTE–ENN and CNN + BiLSTM Integration

      Xu Wang, Weichao Pan, Ruida Liu, Zhen Tian, Keyan Jin

      Abstract

      Parkinson's disease (PD) is the second most prevalent neurodegenerative disorder worldwide. Speech-based diagnostic approaches for PD have attracted increasing attention, with deep learning models demonstrating promising performance. In this paper, we propose a speech-based diagnostic model for PD, aiming to enhance the diagnostic accuracy using deep learning techniques. We adopt the SMOTE–ENN oversampling method to solve the data imbalance problem, and develop a hybrid model that integrates a Convolutional Neural Network (CNN) and Bi-directional Long and Short-Term Memory network (BiLSTM) to efficiently extract the speech features and capture temporal dependencies. Experimental results show that the proposed model achieves an accuracy of 95% on public datasets and outperforms traditional machine learning and other deep learning models in several evaluation metrics, validating the effectiveness of our network in Parkinson's disease diagnosis. These results validate the effectiveness of our approach and highlight its potential for high-precision early screening of PD, offering reliable technical support for clinical applications.