AIMC Topic: Proteins

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Yuel: Improving the Generalizability of Structure-Free Compound-Protein Interaction Prediction.

Journal of chemical information and modeling Feb 1, 2022
Predicting binding affinities between small molecules and the protein target is at the core of computational drug screening and drug target identification. Deep learning-based approaches have recently been adapted to predict binding affinities and th...
Deep Learning Proteins Drug Delivery Systems
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MDL-CPI: Multi-view deep learning model for compound-protein interaction prediction.

Methods (San Diego, Calif.) Jan 31, 2022
Elucidating the mechanisms of Compound-Protein Interactions (CPIs) plays an essential role in drug discovery and development. Many computational efforts have been done to accelerate the development of this field. However, the current predictive perfo...
Software Deep Learning Proteins Cyclopropanes Indoles Neural Networks, Computer
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Cryo-EM and artificial intelligence visualize endogenous protein community members.

Structure (London, England : 1993) Jan 31, 2022
Cellular function is underlined by megadalton assemblies organizing in proximity, forming communities. Metabolons are protein communities involving metabolic pathways such as protein, fatty acid, and thioesters of coenzyme-A synthesis. Metabolons are...
Proteins Artificial Intelligence Ribosomes Cryoelectron Microscopy
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A two-step ensemble learning for predicting protein hot spot residues from whole protein sequence.

Amino acids Jan 30, 2022
Protein hot spot residues are functional sites in protein-protein interactions. Biological experimental methods are traditionally used to identify hot spot residues, which is laborious and time-consuming. Thus a variety of computational methods were ...
Amino Acid Sequence Proteins Databases, Protein Protein Binding Machine Learning
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Testing Precision Limits of Neural Network-Based Quality Control Metrics in High-Throughput Digital Microscopy.

Pharmaceutical research Jan 26, 2022
OBJECTIVE: Digital microscopy is used to monitor particulates such as protein aggregates within biopharmaceutical products. The images that result encode a wealth of information that is underutilized in pharmaceutical process monitoring. For example,...
High-Throughput Screening Assays Protein Aggregates Drug Compounding Proteins Microscopy Image Processing, Computer-Assisted Reproducibility of Results Neural Networks, Computer
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A deep learning approach to predict inter-omics interactions in multi-layer networks.

BMC bioinformatics Jan 26, 2022
BACKGROUND: Despite enormous achievements in the production of high-throughput datasets, constructing comprehensive maps of interactions remains a major challenge. Lack of sufficient experimental evidence on interactions is more significant for heter...
Proteins Algorithms Deep Learning
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Multiple Protein Subcellular Locations Prediction Based on Deep Convolutional Neural Networks with Self-Attention Mechanism.

Interdisciplinary sciences, computational life sciences Jan 23, 2022
As an important research field in bioinformatics, protein subcellular location prediction is critical to reveal the protein functions and provide insightful information for disease diagnosis and drug development. Predicting protein subcellular locati...
Amino Acids Proteins Computational Biology Neural Networks, Computer
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The applications of deep learning algorithms on in silico druggable proteins identification.

Journal of advanced research Jan 22, 2022
INTRODUCTION: The top priority in drug development is to identify novel and effective drug targets. In vitro assays are frequently used for this purpose; however, traditional experimental approaches are insufficient for large-scale exploration of nov...
Algorithms Artificial Intelligence Proteins Neural Networks, Computer Machine Learning Deep Learning
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ProALIGN: Directly Learning Alignments for Protein Structure Prediction via Exploiting Context-Specific Alignment Motifs.

Journal of computational biology : a journal of computational molecular cell biology Jan 21, 2022
Template-based modeling (TBM), including homology modeling and protein threading, is one of the most reliable techniques for protein structure prediction. It predicts protein structure by building an alignment between the query sequence under predict...
Software Algorithms Neural Networks, Computer Proteins Models, Molecular Computational Biology Deep Learning Sequence Analysis, Protein Protein Conformation Amino Acid Motifs Sequence Alignment Amino Acid Sequence
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Learning protein fitness models from evolutionary and assay-labeled data.

Nature biotechnology Jan 17, 2022
Machine learning-based models of protein fitness typically learn from either unlabeled, evolutionarily related sequences or variant sequences with experimentally measured labels. For regimes where only limited experimental data are available, recent ...
Machine Learning Proteins
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