AIMC Topic: Proteins

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Simplified, interpretable graph convolutional neural networks for small molecule activity prediction.

Journal of computer-aided molecular design Nov 24, 2021
We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first conduct a hyperparameter optimization across nearly 800 protein targets that produces a simplified gCNN...
Neural Networks, Computer Proteins
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PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.

Communications biology Nov 19, 2021
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug r...
Machine Learning Protein Binding Proteins Ligands Mutation, Missense
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Structure-Based Drug Design Using Deep Learning.

Journal of chemical information and modeling Nov 18, 2021
In recent years, deep learning-based methods have emerged as promising tools for drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties....
Drug Design Deep Learning Proteins Models, Molecular Ligands
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Protein-Protein Docking: Past, Present, and Future.

The protein journal Nov 17, 2021
The biological significance of proteins attracted the scientific community in exploring their characteristics. The studies shed light on the interaction patterns and functions of proteins in a living body. Due to their practical difficulties, reliabl...
Artificial Intelligence Algorithms Protein Binding Computational Biology Proteins
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Amino acid environment affinity model based on graph attention network.

Journal of bioinformatics and computational biology Nov 13, 2021
Proteins are engines involved in almost all functions of life. They have specific spatial structures formed by twisting and folding of one or more polypeptide chains composed of amino acids. Protein sites are protein structure microenvironments that ...
Neural Networks, Computer Peptides Proteins Amino Acids
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AI reveals structures of protein complexes.

Science (New York, N.Y.) Nov 11, 2021
Artificial Intelligence Software Deep Learning Proteins Fungal Proteins Protein Conformation Protein Interaction Mapping Eukaryota
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Learning the local landscape of protein structures with convolutional neural networks.

Journal of biological physics Nov 9, 2021
One fundamental problem of protein biochemistry is to predict protein structure from amino acid sequence. The inverse problem, predicting either entire sequences or individual mutations that are consistent with a given protein structure, has received...
Neural Networks, Computer Proteins Amino Acids Amino Acid Sequence
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Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

BMC bioinformatics Nov 8, 2021
BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based meth...
Machine Learning Protein Binding Proteins Ligands Binding Sites
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Identification of stress response proteins through fusion of machine learning models and statistical paradigms.

Scientific reports Nov 5, 2021
Proteins are a vital component of cells that perform physiological functions to ensure smooth operations of bodily functions. Identification of a protein's function involves a detailed understanding of the structure of proteins. Stress proteins are e...
Machine Learning Software Models, Statistical Proteins Stress, Physiological
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Assembled graph neural network using graph transformer with edges for protein model quality assessment.

Journal of molecular graphics & modelling Nov 4, 2021
Acquainting protein's structure is of vital importance to accurately understanding its function. Computational method of deep learning has made great progress in protein structure prediction from sequence, and has the potential to help structural bio...
Neural Networks, Computer Proteins
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