AIMC Topic: Proteins

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A hybrid variational autoencoder and WGAN with gradient penalty for tertiary protein structure generation.

Scientific reports Apr 23, 2025
Elucidating the tertiary structure of proteins is important for understanding their functions and interactions. While deep neural networks have advanced the prediction of a protein's native structure from its amino acid sequence, the focus on a singl...
Proteins Models, Molecular Deep Learning Protein Structure, Tertiary Computational Biology Autoencoder Neural Networks, Computer Algorithms
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Advances in artificial intelligence-envisioned technologies for protein and nucleic acid research.

Drug discovery today Apr 17, 2025
Artificial intelligence (AI) and machine learning (ML) have revolutionized pharmaceutical research, particularly in protein and nucleic acid studies. This review summarizes the current status of AI and ML applications in the pharmaceutical sector, fo...
Precision Medicine Animals Artificial Intelligence Machine Learning Proteins Nucleic Acids Humans Drug Discovery Drug Development
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Emerging frontiers in protein structure prediction following the AlphaFold revolution.

Journal of the Royal Society, Interface Apr 16, 2025
Models of protein structures enable molecular understanding of biological processes. Current protein structure prediction tools lie at the interface of biology, chemistry and computer science. Millions of protein structure models have been generated ...
Protein Conformation Protein Folding Proteins Models, Molecular Deep Learning Computational Biology
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LEGOLAS: A Machine Learning Method for Rapid and Accurate Predictions of Protein NMR Chemical Shifts.

Journal of chemical theory and computation Apr 10, 2025
This work introduces LEGOLAS, a fully open source TorchANI-based neural network model designed to predict NMR chemical shifts for protein backbone atoms (N, Cα, Cβ, C', HN, Hα). LEGOLAS has been designed to be fast without loss of accuracy, as our mo...
Neural Networks, Computer Machine Learning Escherichia coli Proteins Nuclear Magnetic Resonance, Biomolecular Molecular Dynamics Simulation Escherichia coli Proteins
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The AI revolution comes to protein sequencing.

Science (New York, N.Y.) Apr 10, 2025
By identifying unknown proteins, new systems could aid research in many areas.
Artificial Intelligence Proteins Sequence Analysis, Protein Humans
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Unified Deep Learning of Molecular and Protein Language Representations with T5ProtChem.

Journal of chemical information and modeling Apr 8, 2025
Deep learning has revolutionized difficult tasks in chemistry and biology, yet existing language models often treat these domains separately, relying on concatenated architectures and independently pretrained weights. These approaches fail to fully e...
Proteins Models, Molecular Deep Learning Computational Biology Drug Discovery
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QuantumBind-RBFE: Accurate Relative Binding Free Energy Calculations Using Neural Network Potentials.

Journal of chemical information and modeling Apr 8, 2025
Accurate prediction of protein-ligand binding affinities is crucial in drug discovery, particularly during hit-to-lead and lead optimization phases, however, limitations in ligand force fields continue to impact prediction accuracy. In this work, we ...
Thermodynamics Quantum Theory Ligands Neural Networks, Computer Protein Binding Proteins Molecular Dynamics Simulation
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Edge-enhanced interaction graph network for protein-ligand binding affinity prediction.

PloS one Apr 8, 2025
Protein-ligand interactions are crucial in drug discovery. Accurately predicting protein-ligand binding affinity is essential for screening potential drugs. Graph neural networks have proven highly effective in modeling spatial relationships and thre...
Proteins Drug Discovery Protein Binding Ligands Neural Networks, Computer Algorithms
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Persistent Directed Flag Laplacian (PDFL)-Based Machine Learning for Protein-Ligand Binding Affinity Prediction.

Journal of chemical theory and computation Apr 5, 2025
Directionality in molecular and biomolecular networks plays an important role in the accurate representation of the complex, dynamic, and asymmetrical nature of interactions present in protein-ligand binding, signal transduction, and biological pathw...
Machine Learning Ligands Protein Binding Proteins
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RPI-GGCN: Prediction of RNA-Protein Interaction Based on Interpretability Gated Graph Convolution Neural Network and Co-Regularized Variational Autoencoders.

IEEE transactions on neural networks and learning systems Apr 4, 2025
RNA-protein interactions (RPIs) play an important role in several fundamental cellular physiological processes, including cell motility, chromosome replication, transcription and translation, and signaling. Predicting RPI can guide the exploration of...
Humans Proteins Computational Biology RNA RNA-Binding Proteins Autoencoder Neural Networks, Computer Algorithms
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