AIMC Topic: Graph Neural Networks

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Prediction of peptide cleavage sites using protein language models and graph neural networks.

Scientific reports Oct 30, 2025
The growing interest in using peptide molecules as therapeutic agents, driven by their high selectivity and efficacy, has become a significant trend in the pharmaceutical industry. However, their oral administration remains challenging due to their l...
Computer Simulation Peptides Computational Biology Neural Networks, Computer Machine Learning Algorithms Proteins Amino Acids Proteolysis Graph Neural Networks Peptides, Cyclic
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Toxigraphnet: a graph neural network framework for precise toxicity prediction of drug molecules.

Journal of computer-aided molecular design Oct 24, 2025
Accurate prediction of a drug molecule's toxicity is a critical step in pharmaceutical research, offering the potential to reduce experimental costs, mitigate adverse effects, and accelerate drug development. Traditional computational methods often r...
Algorithms Pharmaceutical Preparations Lethal Dose 50 Humans Graph Neural Networks Drug-Related Side Effects and Adverse Reactions Neural Networks, Computer Quantitative Structure-Activity Relationship
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ML-PLA: Enhancing Protein-Ligand Binding Affinity Prediction with Microenvironment and Long-Range Interaction-Aware Graph Neural Networks.

Journal of chemical information and modeling Oct 3, 2025
Accurately predicting protein-ligand binding affinity (PLA) is essential in drug discovery for identifying lead compounds. The sequence and structural contexts of an amino acid residue (i.e., microenvironment) describe the surrounding chemical proper...
Proteins Ligands Graph Neural Networks Drug Discovery Protein Binding Neural Networks, Computer
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FatigueNet: A hybrid graph neural network and transformer framework for real-time multimodal fatigue detection.

Scientific reports Sep 30, 2025
Fatigue creates complex challenges that present themselves through cognitive problems alongside physical impacts and emotional consequences. FatigueNet represents a modern multimodal framework that deals with two main weaknesses in present-day fatigu...
Blinking Fatigue Graph Neural Networks Galvanic Skin Response Algorithms Signal Processing, Computer-Assisted Electrocardiography Electromyography Humans Neural Networks, Computer
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PSCG-Net: A Multiscale Crystal Graph Neural Network for Accelerated Materials Discovery.

Journal of chemical information and modeling Sep 29, 2025
The discovery of new materials is crucial for progress in energy, electronics, and sustainable technology. Traditional machine learning approaches, including graph neural networks (GNNs), often fall short because they cannot capture long-range intera...
Neural Networks, Computer Graph Neural Networks
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Enhancing accuracy of virtual kinase profiling via application of graph neural network to 3D pharmacophore ensembles.

Journal of computer-aided molecular design Sep 26, 2025
Kinase profiling is an essential step in both hit identification and selectivity evaluation. Since in vitro testing of large chemical libraries is costly and time-consuming, a computational approach can be applied to narrow down the reasonable chemic...
Humans Machine Learning Graph Neural Networks Drug Design Neural Networks, Computer Drug Discovery Protein Kinase Inhibitors Models, Molecular Small Molecule Libraries Pharmacophore
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Graph neural networks learn emergent tissue properties from spatial molecular profiles.

Nature communications Sep 25, 2025
Tissue phenotypes, such as metabolic states, inflammation, and tumor properties, emerge from both molecular states and spatial cell organization. Spatial molecular assays provide an unbiased view of tissue architecture, enabling phenotype prediction....
Proteomics Graph Neural Networks Colorectal Neoplasms Breast Neoplasms Phenotype Neural Networks, Computer Humans Female
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A graph neural network-based approach for predicting SARS-CoV-2-human protein interactions from multiview data.

PloS one Sep 25, 2025
The COVID-19 pandemic has demanded urgent and accelerated action toward developing effective therapeutic strategies. Drug repurposing models (in silico) are in high demand and require accurate and reliable molecular interaction data. While experiment...
Graph Neural Networks COVID-19 Host-Pathogen Interactions SARS-CoV-2 Viral Proteins Deep Learning Protein Interaction Maps Humans Drug Repositioning Neural Networks, Computer
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Graph Neural Networks in Modern AI-Aided Drug Discovery.

Chemical reviews Sep 17, 2025
Graph neural networks (GNNs), as topology/structure-aware models within deep learning, have emerged as powerful tools for AI-aided drug discovery (AIDD). By directly operating on molecular graphs, GNNs offer an intuitive and expressive framework for ...
Drug Discovery Graph Neural Networks Humans Deep Learning Neural Networks, Computer
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DGSS: A Dynamic Interaction Graph Neural Network with Specific Substructure Awareness for Drug Synergy Prediction.

Journal of chemical information and modeling Sep 16, 2025
Combination therapy presents a transformative approach to treating complex diseases such as cancer by mitigating toxicity and resistance challenges inherent to monotherapy. A critical gap in current computational methods, however, lies in their inabi...
Antineoplastic Agents Drug Synergism Graph Neural Networks Humans Neural Networks, Computer
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