AIMC Topic: Proteins

Clear Filters Showing 771 to 780 of 1967 articles

Deep generative modeling for protein design.

Current opinion in structural biology Dec 25, 2021
Deep learning approaches have produced substantial breakthroughs in fields such as image classification and natural language processing and are making rapid inroads in the area of protein design. Many generative models of proteins have been developed...
Proteins Neural Networks, Computer
View on PubMed DOI

Artificial intelligence based methods for hot spot prediction.

Current opinion in structural biology Dec 23, 2021
Proteins interact through their interfaces to fulfill essential functions in the cell. They bind to their partners in a highly specific manner and form complexes that have a profound effect on understanding the biological pathways they are involved i...
Databases, Protein Amino Acids Proteins Artificial Intelligence Machine Learning Protein Binding
View on PubMed DOI

DSResSol: A Sequence-Based Solubility Predictor Created with Dilated Squeeze Excitation Residual Networks.

International journal of molecular sciences Dec 17, 2021
Protein solubility is an important thermodynamic parameter that is critical for the characterization of a protein's function, and a key determinant for the production yield of a protein in both the research setting and within industrial (e.g., pharma...
Amino Acid Sequence Solubility Deep Learning Computational Biology Proteins Models, Chemical
View on PubMed DOI

Protein structures for all.

Science (New York, N.Y.) Dec 16, 2021
AI-powered predictions reveal the shapes of proteins by the thousands.
Artificial Intelligence Software Models, Molecular Protein Conformation Proteins Databases, Protein Protein Folding
View on PubMed DOI

Adaptive machine learning for protein engineering.

Current opinion in structural biology Dec 9, 2021
Machine-learning models that learn from data to predict how protein sequence encodes function are emerging as a useful protein engineering tool. However, when using these models to suggest new protein designs, one must deal with the vast combinatoria...
Amino Acid Sequence Protein Engineering Machine Learning Proteins
View on PubMed DOI

InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions.

Journal of medicinal chemistry Dec 8, 2021
Accurate quantification of protein-ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning (ML)-based methods based on handcrafted descriptors, one-dimensional protein sequences, and/or two-di...
Ligands Protein Binding Proteins Algorithms Deep Learning
View on PubMed DOI

Updated Prediction of Aggregators and Assay-Interfering Substructures in Food Compounds.

Journal of agricultural and food chemistry Dec 8, 2021
Positive outcomes in biochemical and biological assays of food compounds may appear due to the well-described capacity of some compounds to form colloidal aggregates that adsorb proteins, resulting in their denaturation and loss of function. This phe...
Machine Learning Proteins Biological Assay Databases, Factual Drug Discovery
View on PubMed DOI

Protein Fold Recognition From Sequences Using Convolutional and Recurrent Neural Networks.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
The identification of a protein fold type from its amino acid sequence provides important insights about the protein 3D structure. In this paper, we propose a deep learning architecture that can process protein residue-level features to address the p...
Proteins Protein Folding Sequence Analysis, Protein Deep Learning Computational Biology Algorithms
View on PubMed DOI

Protein Fold Recognition Based on Auto-Weighted Multi-View Graph Embedding Learning Model.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Protein fold recognition is critical for studies of the protein structure prediction and drug design. Several methods have been proposed to obtain discriminative features from the protein sequences for fold recognition. However, the ensemble methods ...
Computational Biology Sequence Analysis, Protein Proteins Algorithms Machine Learning Protein Folding Databases, Protein Sequence Alignment Amino Acid Sequence
View on PubMed DOI

Prediction of Drug-Target Interactions Based on Network Representation Learning and Ensemble Learning.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2021
Identifying interactions between drugs and target proteins is a critical step in the drug development process, as it helps identify new targets for drugs and accelerate drug development. The number of known drug-protein interactions (positive samples...
Algorithms Computational Biology Drug Development Decision Trees Drug Interactions Humans Proteins Machine Learning Pharmaceutical Preparations
View on PubMed DOI
Popular Topics
Recent Journals