AI Medical Compendium Topic:
Proteins

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

International journal of molecular sciences
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...

Protein structures for all.

Science (New York, N.Y.)
AI-powered predictions reveal the shapes of proteins by the thousands.

Adaptive machine learning for protein engineering.

Current opinion in structural biology
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...

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

Journal of medicinal chemistry
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...

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

Journal of agricultural and food chemistry
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...

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

IEEE/ACM transactions on computational biology and bioinformatics
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...

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

IEEE/ACM transactions on computational biology and bioinformatics
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 ...

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

IEEE/ACM transactions on computational biology and bioinformatics
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...

Multi-Neighborhood Learning for Global Alignment in Biological Networks.

IEEE/ACM transactions on computational biology and bioinformatics
The global alignment of biological networks (GABN) aims to find an optimal alignment between proteins across species, such that both the biological structures and the topological structures of the proteins are maximally conserved. The research on GAB...

Categorical Matrix Completion With Active Learning for High-Throughput Screening.

IEEE/ACM transactions on computational biology and bioinformatics
The recent advances in wet-lab automation enable high-throughput experiments to be conducted seamlessly. In particular, the exhaustive enumeration of all possible conditions is always involved in high-throughput screening. Nonetheless, such a screeni...