AIMC Topic: Proteins

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Complex machine learning model needs complex testing: Examining predictability of molecular binding affinity by a graph neural network.

Journal of computational chemistry Feb 24, 2022
Drug discovery pipelines typically involve high-throughput screening of large amounts of compounds in a search of potential drugs candidates. As a chemical space of small organic molecules is huge, a "navigation" over it urges for fast and lightweigh...
Neural Networks, Computer Machine Learning Protein Binding Proteins Databases, Protein Ligands
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Structure-Aware Multimodal Deep Learning for Drug-Protein Interaction Prediction.

Journal of chemical information and modeling Feb 24, 2022
Identifying drug-protein interactions (DPIs) is crucial in drug discovery, and a number of machine learning methods have been developed to predict DPIs. Existing methods usually use unrealistic data sets with hidden bias, which will limit the accurac...
Neural Networks, Computer Machine Learning Deep Learning Proteins Amino Acid Sequence
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TMPpred: A support vector machine-based thermophilic protein identifier.

Analytical biochemistry Feb 23, 2022
MOTIVATION: The thermostability of proteins will cause them to break the temperature binding and play more functions. Using machine learning, we explored the mechanism of and reasons for protein thermostability characteristics.
Machine Learning Support Vector Machine Proteins
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DLPacker: Deep learning for prediction of amino acid side chain conformations in proteins.

Proteins Feb 22, 2022
Prediction of side chain conformations of amino acids in proteins (also termed "packing") is an important and challenging part of protein structure prediction with many interesting applications in protein design. A variety of methods for packing have...
Neural Networks, Computer Deep Learning Proteins Amino Acids Protein Conformation
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Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network machine learning model.

BMC bioinformatics Feb 19, 2022
BACKGROUND: The investigation of possible interactions between two proteins in intracellular signaling is an expensive and laborious procedure in the wet-lab, therefore, several in silico approaches have been implemented to narrow down the candidates...
Machine Learning Algorithms ROC Curve Protein Interaction Maps Proteins
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Residue-Frustration-Based Prediction of Protein-Protein Interactions Using Machine Learning.

The journal of physical chemistry. B Feb 16, 2022
The study of protein-protein interactions (PPIs) is important in understanding the function of proteins. However, it is still a challenge to investigate the transient protein-protein interaction by experiments. Hence, the computational prediction for...
Neural Networks, Computer Machine Learning Algorithms Computational Biology Proteins Protein Interaction Mapping
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Prediction of liquid-liquid phase separating proteins using machine learning.

BMC bioinformatics Feb 15, 2022
BACKGROUND: The liquid-liquid phase separation (LLPS) of biomolecules in cell underpins the formation of membraneless organelles, which are the condensates of protein, nucleic acid, or both, and play critical roles in cellular function. Dysregulation...
Humans Machine Learning Proteins Organelles
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ProtPlat: an efficient pre-training platform for protein classification based on FastText.

BMC bioinformatics Feb 11, 2022
BACKGROUND: For the past decades, benefitting from the rapid growth of protein sequence data in public databases, a lot of machine learning methods have been developed to predict physicochemical properties or functions of proteins using amino acid se...
Neural Networks, Computer Machine Learning Proteins Natural Language Processing Amino Acid Sequence
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Machine learning modeling of family wide enzyme-substrate specificity screens.

PLoS computational biology Feb 10, 2022
Biocatalysis is a promising approach to sustainably synthesize pharmaceuticals, complex natural products, and commodity chemicals at scale. However, the adoption of biocatalysis is limited by our ability to select enzymes that will catalyze their nat...
Machine Learning Drug Discovery Proteins Amino Acid Sequence Substrate Specificity
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A backbone-centred energy function of neural networks for protein design.

Nature Feb 9, 2022
A protein backbone structure is designable if a substantial number of amino acid sequences exist that autonomously fold into it. It has been suggested that the designability of backbones is governed mainly by side chain-independent or side chain type...
Neural Networks, Computer Proteins Models, Molecular Amino Acid Sequence Protein Conformation
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