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Models, Molecular

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Lessons from Deep Learning Structural Prediction of Multistate Multidomain Proteins-The Case Study of Coiled-Coil NOD-like Receptors.

International journal of molecular sciences 39859213
We test here the prediction capabilities of the new generation of deep learning predictors in the more challenging situation of multistate multidomain proteins by using as a case study a coiled-coil family of Nucleotide-binding Oligomerization Domain...
Arabidopsis Computational Biology Deep Learning Models, Molecular NLR Proteins Protein Conformation Protein Domains Protein Structure, Secondary
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MVGNN-PPIS: A novel multi-view graph neural network for protein-protein interaction sites prediction based on Alphafold3-predicted structures and transfer learning.

International journal of biological macromolecules 39848362
Protein-protein interactions (PPI) are crucial for understanding numerous biological processes and pathogenic mechanisms. Identifying interaction sites is essential for biomedical research and targeted drug development. Compared to experimental metho...
Algorithms Binding Sites Computational Biology Graph Neural Networks Models, Molecular Neural Networks, Computer Protein Binding Protein Conformation Protein Interaction Mapping Proteins
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Deciphering Protein Secondary Structures and Nucleic Acids in Cryo-EM Maps Using Deep Learning.

Journal of chemical information and modeling 39838545
With the resolution revolution of cryo-electron microscopy (cryo-EM) and the rapid development of image processing technology, cryo-EM has become an indispensable experimental method for determining the three-dimensional structures of biological macr...
Cryoelectron Microscopy Deep Learning Models, Molecular Nucleic Acids Protein Structure, Secondary Proteins
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Targeting protein-ligand neosurfaces with a generalizable deep learning tool.

Nature 39814890
Molecular recognition events between proteins drive biological processes in living systems. However, higher levels of mechanistic regulation have emerged, in which protein-protein interactions are conditioned to small molecules. Despite recent advanc...
Bridged Bicyclo Compounds, Heterocyclic Deep Learning Drug Design Humans Ligands Models, Molecular Protein Binding Proteins Proto-Oncogene Proteins c-bcl-2
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CovCysPredictor: Predicting Selective Covalently Modifiable Cysteines Using Protein Structure and Interpretable Machine Learning.

Journal of chemical information and modeling 39780548
Targeted covalent inhibition is a powerful therapeutic modality in the drug discoverer's toolbox. Recent advances in covalent drug discovery, in particular, targeting cysteines, have led to significant breakthroughs for traditionally challenging targ...
Cysteine Databases, Protein Drug Discovery Humans Ligands Machine Learning Models, Molecular Protein Conformation Proteins
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Data and AI-driven synthetic binding protein discovery.

Trends in pharmacological sciences 39755458
Synthetic binding proteins (SBPs) are a class of protein binders that are artificially created and do not exist naturally. Their broad applications in tackling challenges of research, diagnostics, and therapeutics have garnered significant interest. ...
Animals Artificial Intelligence Computational Biology Drug Discovery Humans Models, Molecular Protein Binding Protein Engineering Proteins
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On the emergence of machine-learning methods in bottom-up coarse-graining.

Current opinion in structural biology 39752847
Machine-learning methods have gained significant attention in the computational chemistry community as a viable approach to molecular modeling and analysis. Recent successes in utilizing neural networks to learn atomistic force-fields which 'coarse-g...
Machine Learning Models, Molecular Molecular Dynamics Simulation Neural Networks, Computer Thermodynamics
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DPFunc: accurately predicting protein function via deep learning with domain-guided structure information.

Nature communications 39746897
Computational methods for predicting protein function are of great significance in understanding biological mechanisms and treating complex diseases. However, existing computational approaches of protein function prediction lack interpretability, mak...
Algorithms Computational Biology Databases, Protein Deep Learning Humans Models, Molecular Protein Conformation Protein Domains Proteins Structure-Activity Relationship
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AFFIPred: AlphaFold2 structure-based Functional Impact Prediction of missense variations.

Protein science : a publication of the Protein Society 39840793
Protein structure holds immense potential for pathogenicity prediction, albeit structure-based predictors are limited compared to the sequence-based counterparts due to the "structure knowledge gap" between large number of available protein sequences...
Computational Biology Databases, Protein Humans Machine Learning Models, Molecular Mutation, Missense Protein Conformation Proteins Software
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AggNet: Advancing protein aggregation analysis through deep learning and protein language model.

Protein science : a publication of the Protein Society 39840791
Protein aggregation is critical to various biological and pathological processes. Besides, it is also an important property in biotherapeutic development. However, experimental methods to profile protein aggregation are costly and labor-intensive, dr...
Deep Learning Humans Models, Molecular Protein Aggregates Proteins
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