AIMC Topic: Graph Neural Networks

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Development of Coarse-Grained Lipid Force Fields Based on a Graph Neural Network.

Journal of chemical theory and computation Sep 10, 2025
Coarse-grained (CG) lipid models enable efficient simulations of large-scale membrane events. However, achieving both speed and atomic-level accuracy remains challenging. Graph neural networks (GNNs) trained on all-atom (AA) simulations can serve as ...
Neural Networks, Computer Lipids Molecular Dynamics Simulation Lipid Bilayers Graph Neural Networks
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Multilevel Fusion Graph Neural Network for Molecule Property Prediction.

Journal of chemical information and modeling Aug 20, 2025
Accurate prediction of molecular properties is essential in drug discovery and related fields. However, existing graph neural networks (GNNs) often struggle to simultaneously capture both local and global molecular structures. In this work, we propos...
Neural Networks, Computer Drug Discovery Graph Neural Networks
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Graph neural network-based drug-drug interaction prediction.

Scientific reports Aug 19, 2025
With the growing variety of pharmacological compounds and the increasing need for polypharmacy, accurately predicting drug-drug interactions (DDIs) is essential to ensure both treatment efficacy and patient safety. Beneficial DDIs can enhance therape...
Humans Neural Networks, Computer Algorithms Drug Interactions Graph Neural Networks
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MEGDTA: multi-modal drug-target affinity prediction based on protein three-dimensional structure and ensemble graph neural network.

BMC genomics Aug 11, 2025
BACKGROUND: Drug development is a time-consuming and costly endeavor, and utilizing computer-aided methods to predict drug-target affinity (DTA) can significantly accelerate this process. The key to accurate DTA prediction lies in selecting appropria...
Neural Networks, Computer Computational Biology Proteins Protein Conformation Graph Neural Networks
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Distilling knowledge from graph neural networks trained on cell graphs to non-neural student models.

Scientific reports Aug 10, 2025
The development and refinement of artificial intelligence (AI) and machine learning algorithms have been an area of intense research in radiology and pathology, particularly for automated or computer-aided diagnosis. Whole Slide Imaging (WSI) has eme...
Humans Neural Networks, Computer Machine Learning Algorithms Image Processing, Computer-Assisted Graph Neural Networks
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GNN-RMNet: Leveraging graph neural networks and GPS analytics for driver behavior and route optimization in logistics.

PloS one Aug 7, 2025
Logistics networks are becoming increasingly complex and rely more heavily on real-time vehicle data, necessitating intelligent systems to monitor driver behavior and identify route anomalies. Traditional techniques struggle to capture the dynamic sp...
Humans Neural Networks, Computer Algorithms Geographic Information Systems Deep Learning Automobile Driving Graph Neural Networks
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PairReg: A method for enhancing the learning of molecular structure representation in equivariant graph neural networks.

PloS one Jul 31, 2025
The 3D structure of molecules contains a wealth of important information, but traditional 3DCNN-based methods fail to adequately address the transformations of rigid motions (rotation, translation, and mapping). Equivariant graph neural networks (EGN...
Neural Networks, Computer Algorithms Models, Molecular Molecular Structure Graph Neural Networks
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Integration of pre-trained protein language models with equivariant graph neural networks for peptide toxicity prediction.

BMC biology Jul 28, 2025
BACKGROUND: Peptide-based therapeutics have great potential due to their versatility, high specificity, and suitability for a variety of therapeutic applications. Despite these advantages, the inherent toxicities of some peptides pose challenges in d...
Neural Networks, Computer Peptides Graph Neural Networks
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Quantum-Embedded Graph Neural Network Architecture for Molecular Property Prediction.

Journal of chemical information and modeling Jul 26, 2025
Accurate prediction of molecular properties is crucial for accelerating the development of new drugs, and quantum machine learning (QML) holds great promise in this domain. A typical QML pipeline comprises two core stages: encoding classical data int...
Neural Networks, Computer Machine Learning Quantum Theory Graph Neural Networks
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Explainable Graph Neural Networks in Chemistry: Combining Attribution and Uncertainty Quantification.

Journal of chemical information and modeling Jul 17, 2025
Graph Neural Networks (GNNs) are powerful tools for predicting chemical properties, but their black-box nature can limit trust and utility. Explainability through feature attribution and awareness of prediction uncertainty are critical for practical ...
Neural Networks, Computer Solubility Molecular Weight Uncertainty Graph Neural Networks
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