AI Medical Compendium Topic:
Proteins

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Molecule Design Using Molecular Generative Models Constrained by Ligand-Protein Interactions.

Journal of chemical information and modeling
In recent years, molecular deep generative models have attracted much attention for its application in drug design. The data-driven molecular deep generative model approximates the high dimensional distribution of the chemical space through learning...

ProB-Site: Protein Binding Site Prediction Using Local Features.

Cells
Protein-protein interactions (PPIs) are responsible for various essential biological processes. This information can help develop a new drug against diseases. Various experimental methods have been employed for this purpose; however, their applicatio...

A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database.

Biomolecules
Calculation of protein-ligand binding affinity is a cornerstone of drug discovery. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, o...

GalaxyWater-CNN: Prediction of Water Positions on the Protein Structure by a 3D-Convolutional Neural Network.

Journal of chemical information and modeling
Proteins interact with numerous water molecules to perform their physiological functions in biological organisms. Most water molecules act as solvent media; hence, their roles may be considered implicitly in theoretical treatments of protein structur...

Interpretable pairwise distillations for generative protein sequence models.

PLoS computational biology
Many different types of generative models for protein sequences have been proposed in literature. Their uses include the prediction of mutational effects, protein design and the prediction of structural properties. Neural network (NN) architectures h...

Encoder-decoder neural networks for predicting future FTIR spectra - application to enzymatic protein hydrolysis.

Journal of biophotonics
In the process of converting food-processing by-products to value-added ingredients, fine grained control of the raw materials, enzymes and process conditions ensures the best possible yield and economic return. However, when raw material batches lac...

PIF - A Java library for finding atomic interactions and extracting geometric features supporting the analysis of protein structures.

Methods (San Diego, Calif.)
Proteins play an essential role in the functioning of living organisms. The enormity of the atomic interactions in proteins is essential in controlling their spatial structures and dynamics. It can also provide scientists with valuable information th...

Protein Subcellular Localization Prediction Model Based on Graph Convolutional Network.

Interdisciplinary sciences, computational life sciences
Protein subcellular localization prediction is an important research area in bioinformatics, which plays an essential role in understanding protein function and mechanism. Many machine learning and deep learning algorithms have been employed for this...

Automated Protein Secondary Structure Assignment from C Positions Using Neural Networks.

Biomolecules
The assignment of secondary structure elements in protein conformations is necessary to interpret a protein model that has been established by computational methods. The process essentially involves labeling the amino acid residues with H (Helix), E ...

Sequence-based drug-target affinity prediction using weighted graph neural networks.

BMC genomics
BACKGROUND: Affinity prediction between molecule and protein is an important step of virtual screening, which is usually called drug-target affinity (DTA) prediction. Its accuracy directly influences the progress of drug development. Sequence-based d...