AIMC Topic: Proteins

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Comparative analysis of network-based approaches and machine learning algorithms for predicting drug-target interactions.

Methods (San Diego, Calif.) Nov 1, 2021
Computational prediction of drug-target interactions (DTIs) is of particular importance in the process of drug repositioning because of its efficiency in selecting potential candidates for DTIs. A variety of computational methods for predicting DTIs ...
Proteins Drug Repositioning Drug Interactions Machine Learning Algorithms
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Protein Design with Deep Learning.

International journal of molecular sciences Oct 29, 2021
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using De...
Protein Engineering Proteins Protein Domains Deep Learning Computational Biology
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End-to-end learning for compound activity prediction based on binding pocket information.

BMC bioinformatics Oct 29, 2021
BACKGROUND: Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, do...
Amino Acid Sequence Ligands Protein Binding Proteins Neural Networks, Computer Machine Learning Molecular Docking Simulation
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adabmDCA: adaptive Boltzmann machine learning for biological sequences.

BMC bioinformatics Oct 29, 2021
BACKGROUND: Boltzmann machines are energy-based models that have been shown to provide an accurate statistical description of domains of evolutionary-related protein and RNA families. They are parametrized in terms of local biases accounting for resi...
Proteins RNA Humans Machine Learning
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DeepLC can predict retention times for peptides that carry as-yet unseen modifications.

Nature methods Oct 28, 2021
The inclusion of peptide retention time prediction promises to remove peptide identification ambiguity in complex liquid chromatography-mass spectrometry identification workflows. However, due to the way peptides are encoded in current prediction mod...
Proteins Peptide Fragments Peptide Mapping Datasets as Topic Proteome Protein Processing, Post-Translational Algorithms Deep Learning Humans
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Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.

Journal of computer-aided molecular design Oct 28, 2021
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-re...
Thermodynamics Proteins Ligands Molecular Docking Simulation Drug Discovery Machine Learning
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Crowdsourcing biocuration: The Community Assessment of Community Annotation with Ontologies (CACAO).

PLoS computational biology Oct 28, 2021
Experimental data about gene functions curated from the primary literature have enormous value for research scientists in understanding biology. Using the Gene Ontology (GO), manual curation by experts has provided an important resource for studying ...
Humans Gene Ontology Crowdsourcing Proteins Molecular Sequence Annotation Computational Biology Databases, Genetic
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Disease variant prediction with deep generative models of evolutionary data.

Nature Oct 27, 2021
Quantifying the pathogenicity of protein variants in human disease-related genes would have a marked effect on clinical decisions, yet the overwhelming majority (over 98%) of these variants still have unknown consequences. In principle, computational...
Biological Assay Genetic Variation Models, Molecular Bayes Theorem Genetic Fitness Disease Selection, Genetic Phenotype Genetic Predisposition to Disease Proteins Unsupervised Machine Learning Evolution, Molecular Humans
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A Graph Feature Auto-Encoder for the prediction of unobserved node features on biological networks.

BMC bioinformatics Oct 27, 2021
BACKGROUND: Molecular interaction networks summarize complex biological processes as graphs, whose structure is informative of biological function at multiple scales. Simultaneously, omics technologies measure the variation or activity of genes, prot...
Neural Networks, Computer Animals Proteins Computational Biology Escherichia coli Gene Regulatory Networks Mice
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Accurate prediction of protein torsion angles using evolutionary signatures and recurrent neural network.

Scientific reports Oct 26, 2021
The amino acid sequence of a protein contains all the necessary information to specify its shape, which dictates its biological activities. However, it is challenging and expensive to experimentally determine the three-dimensional structure of protei...
Protein Structure, Secondary Protein Structure, Tertiary Databases, Protein Predictive Value of Tests Neural Networks, Computer Evolution, Molecular Proteins Software
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