AIMC Topic: Proteins

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Deep Learning-Based Ligand Design Using Shared Latent Implicit Fingerprints from Collaborative Filtering.

Journal of chemical information and modeling Apr 26, 2021
In their previous work, Srinivas et al. [ 2018, 10, 56] have shown that implicit fingerprints capture ligands and proteins in a shared latent space, typically for the purposes of virtual screening with collaborative filtering models applied on known...
Reproducibility of Results Deep Learning Ligands Proteins
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Convolutional neural networks with image representation of amino acid sequences for protein function prediction.

Computational biology and chemistry Apr 24, 2021
Proteins are one of the most important molecules that govern the cellular processes in most of the living organisms. Various functions of the proteins are of paramount importance to understand the basics of life. Several supervised learning approache...
Proteins Neural Networks, Computer Databases, Protein Deep Learning Amino Acid Sequence
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Deep Learning in Virtual Screening: Recent Applications and Developments.

International journal of molecular sciences Apr 23, 2021
Drug discovery is a cost and time-intensive process that is often assisted by computational methods, such as virtual screening, to speed up and guide the design of new compounds. For many years, machine learning methods have been successfully applied...
Deep Learning Computational Biology Technology, Pharmaceutical Proteins Drug Design Drug Discovery Humans Neural Networks, Computer
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Protein design and variant prediction using autoregressive generative models.

Nature communications Apr 23, 2021
The ability to design functional sequences and predict effects of variation is central to protein engineering and biotherapeutics. State-of-art computational methods rely on models that leverage evolutionary information but are inadequate for importa...
Antigens Protein Engineering Computational Biology Proteins Antibodies Amino Acid Sequence Mutation Phenotype Neural Networks, Computer Genotype Algorithms Humans
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PCprophet: a framework for protein complex prediction and differential analysis using proteomic data.

Nature methods Apr 15, 2021
Despite the availability of methods for analyzing protein complexes, systematic analysis of complexes under multiple conditions remains challenging. Approaches based on biochemical fractionation of intact, native complexes and correlation of protein ...
Machine Learning Software Proteomics Bayes Theorem Protein Conformation Chromatography, Gel Proteins Databases, Protein
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A Cascade Graph Convolutional Network for Predicting Protein-Ligand Binding Affinity.

International journal of molecular sciences Apr 14, 2021
Accurate prediction of binding affinity between protein and ligand is a very important step in the field of drug discovery. Although there are many methods based on different assumptions and rules do exist, prediction performance of protein-ligand bi...
Proteins Neural Networks, Computer Databases, Protein Ligands Protein Binding Models, Theoretical
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The whole is greater than its parts: ensembling improves protein contact prediction.

Scientific reports Apr 13, 2021
The prediction of amino acid contacts from protein sequence is an important problem, as protein contacts are a vital step towards the prediction of folded protein structures. We propose that a powerful concept from deep learning, called ensembling, c...
Computational Biology Proteins Protein Conformation Sequence Analysis, Protein Protein Folding Models, Molecular Algorithms Neural Networks, Computer Deep Learning Software
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Development of a graph convolutional neural network model for efficient prediction of protein-ligand binding affinities.

PloS one Apr 8, 2021
Prediction of protein-ligand interactions is a critical step during the initial phase of drug discovery. We propose a novel deep-learning-based prediction model based on a graph convolutional neural network, named GraphBAR, for protein-ligand binding...
Protein Binding Drug Discovery Neural Networks, Computer Ligands Proteins Deep Learning Molecular Docking Simulation
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Protein Complexes Detection Based on Semi-Supervised Network Embedding Model.

IEEE/ACM transactions on computational biology and bioinformatics Apr 8, 2021
A protein complex is a group of associated polypeptide chains which plays essential roles in the biological process. Given a graph representing protein-protein interactions (PPI) network, it is critical but non-trivial to detect protein complexes, th...
Proteins Supervised Machine Learning Protein Interaction Mapping Computational Biology Algorithms Cluster Analysis Protein Interaction Maps
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Analyzing effect of quadruple multiple sequence alignments on deep learning based protein inter-residue distance prediction.

Scientific reports Apr 7, 2021
Protein 3D structure prediction has advanced significantly in recent years due to improving contact prediction accuracy. This improvement has been largely due to deep learning approaches that predict inter-residue contacts and, more recently, distanc...
Deep Learning Proteins Models, Molecular Neural Networks, Computer Protein Structure, Tertiary Sequence Analysis, Protein Protein Interaction Domains and Motifs Caspases Sequence Alignment
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