AIMC Topic: Proteins

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Graph Convolutional Neural Networks for Predicting Drug-Target Interactions.

Journal of chemical information and modeling Oct 16, 2019
Accurate determination of target-ligand interactions is crucial in the drug discovery process. In this paper, we propose a graph-convolutional (Graph-CNN) framework for predicting protein-ligand interactions. First, we built an unsupervised graph-aut...
Neural Networks, Computer Drug Discovery Protein Binding Databases, Chemical Protein Conformation Binding Sites Drug Development Proteins Ligands
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Recent developments in deep learning applied to protein structure prediction.

Proteins Oct 14, 2019
Although many structural bioinformatics tools have been using neural network models for a long time, deep neural network (DNN) models have attracted considerable interest in recent years. Methods employing DNNs have had a significant impact in recent...
Neural Networks, Computer Deep Learning Computational Biology Proteins Models, Molecular Protein Conformation Structural Homology, Protein
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Unsupervised and Supervised Learning over theEnergy Landscape for Protein Decoy Selection.

Biomolecules Oct 14, 2019
The energy landscape that organizes microstates of a molecular system and governs theunderlying molecular dynamics exposes the relationship between molecular form/structure, changesto form, and biological activity or function in the cell. However, se...
Protein Conformation Proteins Thermodynamics Databases, Protein Supervised Machine Learning
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Investigation of machine learning techniques on proteomics: A comprehensive survey.

Progress in biophysics and molecular biology Sep 27, 2019
Proteomics is the extensive investigation of proteins which has empowered the recognizable proof of consistently expanding quantities of protein. Proteins are necessary part of living life form, with numerous capacities. The proteome is the complete ...
Proteins Databases, Protein Protein Conformation Torsion, Mechanical Computational Biology Proteomics Proteome Machine Learning Computer Simulation
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All-Assay-Max2 pQSAR: Activity Predictions as Accurate as Four-Concentration ICs for 8558 Novartis Assays.

Journal of chemical information and modeling Sep 26, 2019
Profile-quantitative structure-activity relationship (pQSAR) is a massively multitask, two-step machine learning method with unprecedented scope, accuracy, and applicability domain. In step one, a "profile" of conventional single-assay random forest ...
Machine Learning Algorithms Inhibitory Concentration 50 Quantitative Structure-Activity Relationship Proteins Drug Discovery Models, Chemical Logistic Models Biological Assay Dose-Response Relationship, Drug
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Analysis of distance-based protein structure prediction by deep learning in CASP13.

Proteins Sep 13, 2019
This paper reports the CASP13 results of distance-based contact prediction, threading, and folding methods implemented in three RaptorX servers, which are built upon the powerful deep convolutional residual neural network (ResNet) method initiated by...
Sequence Alignment Sequence Analysis, Protein Crystallography, X-Ray Protein Conformation Protein Folding Computational Biology Proteins Models, Molecular Machine Learning Software Algorithms Deep Learning
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From Target to Drug: Generative Modeling for the Multimodal Structure-Based Ligand Design.

Molecular pharmaceutics Sep 10, 2019
Chemical space is impractically large, and conventional structure-based virtual screening techniques cannot be used to simply search through the entire space to discover effective bioactive molecules. To address this shortcoming, we propose a generat...
Humans Proteins Models, Chemical Ligands Neural Networks, Computer Drug Design Drug Discovery Molecular Conformation Quantitative Structure-Activity Relationship Small Molecule Libraries
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Predicting Drug-Target Interaction Using a Novel Graph Neural Network with 3D Structure-Embedded Graph Representation.

Journal of chemical information and modeling Sep 6, 2019
We propose a novel deep learning approach for predicting drug-target interaction using a graph neural network. We introduce a distance-aware graph attention algorithm to differentiate various types of intermolecular interactions. Furthermore, we extr...
Protein Conformation Computer Graphics Models, Molecular Ligands Molecular Targeted Therapy Neural Networks, Computer Algorithms Computational Biology Proteins
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Getting to Know Your Neighbor: Protein Structure Prediction Comes of Age with Contextual Machine Learning.

Journal of computational biology : a journal of computational molecular cell biology Aug 30, 2019
The folding of a protein structure is a process governed by both local and nonlocal interactions. While incorporating local dependencies into a machine learning algorithm for protein structure prediction is simple and has been exploited for some time...
Protein Conformation Algorithms Neural Networks, Computer Aging Machine Learning Proteins
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Boosting phosphorylation site prediction with sequence feature-based machine learning.

Proteins Aug 22, 2019
Protein phosphorylation is one of the essential posttranslation modifications playing a vital role in the regulation of many fundamental cellular processes. We propose a LightGBM-based computational approach that uses evolutionary, geometric, sequenc...
Binding Sites Serine Sequence Analysis, Protein Protein Conformation Species Specificity Algorithms Computational Biology Tyrosine Humans Reproducibility of Results Protein Processing, Post-Translational Databases, Protein Animals Machine Learning Threonine Proteins Phosphorylation Models, Molecular
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