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Binding Sites

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DeepConv-DTI: Prediction of drug-target interactions via deep learning with convolution on protein sequences.

PLoS computational biology Jun 14, 2019
Identification of drug-target interactions (DTIs) plays a key role in drug discovery. The high cost and labor-intensive nature of in vitro and in vivo experiments have highlighted the importance of in silico-based DTI prediction approaches. In severa...
Proteins Models, Molecular Amino Acid Sequence Ligands Binding Sites Sequence Analysis, Protein Drug Discovery Deep Learning Computational Biology Computer Simulation
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A Machine Learning Approach for the Discovery of Ligand-Specific Functional Mechanisms of GPCRs.

Molecules (Basel, Switzerland) Jun 2, 2019
G protein-coupled receptors (GPCRs) play a key role in many cellular signaling mechanisms, and must select among multiple coupling possibilities in a ligand-specific manner in order to carry out a myriad of functions in diverse cellular contexts. Muc...
Ligands Receptor, Serotonin, 5-HT2A Molecular Docking Simulation Protein Binding Quantitative Structure-Activity Relationship Receptors, G-Protein-Coupled Drug Design Binding Sites Molecular Dynamics Simulation Humans Drug Discovery Machine Learning Algorithms
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DP-BINDER: machine learning model for prediction of DNA-binding proteins by fusing evolutionary and physicochemical information.

Journal of computer-aided molecular design May 23, 2019
DNA-binding proteins (DBPs) participate in various biological processes including DNA replication, recombination, and repair. In the human genome, about 6-7% of these proteins are utilized for genes encoding. DBPs shape the DNA into a compact structu...
DNA-Binding Proteins Binding Sites Position-Specific Scoring Matrices Evolution, Molecular Databases, Protein DNA Humans Machine Learning Algorithms Animals Support Vector Machine
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Prediction of zinc-binding sites using multiple sequence profiles and machine learning methods.

Molecular omics May 2, 2019
The zinc (Zn) cofactor has been proven to be involved in numerous biological mechanisms and the zinc-binding site is recognized as one of the most important post-translation modifications in proteins. Therefore, accurate knowledge of zinc ions in pro...
Algorithms Computer Simulation Zinc Machine Learning Software Databases, Protein Amino Acid Sequence Protein Conformation Protein Binding Computational Biology Support Vector Machine Binding Sites Protein Folding
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MD-SVM: a novel SVM-based algorithm for the motif discovery of transcription factor binding sites.

BMC bioinformatics May 1, 2019
BACKGROUND: Transcription factors (TFs) play important roles in the regulation of gene expression. They can activate or block transcription of downstream genes in a manner of binding to specific genomic sequences. Therefore, motif discovery of these ...
Nucleotide Motifs Humans Algorithms Protein Binding Transcription Factors Support Vector Machine Binding Sites
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Prediction of Protein Metal Binding Sites Using Deep Neural Networks.

Molecular informatics Apr 12, 2019
Metals have crucial roles for many physiological, pathological and diagnostic processes. Metal binding proteins or metalloproteins are important for metabolism functions. The proteins that reach the three-dimensional structure by folding show which v...
Binding Sites Deep Learning Metals Proteins
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RNA-Protein Binding Sites Prediction via Multi Scale Convolutional Gated Recurrent Unit Networks.

IEEE/ACM transactions on computational biology and bioinformatics Apr 11, 2019
RNA-Protein binding plays important roles in the field of gene expression. With the development of high throughput sequencing, several conventional methods and deep learning-based methods have been proposed to predict the binding preference of RNA-pr...
Neural Networks, Computer Algorithms Protein Binding Computational Biology RNA-Binding Proteins RNA Binding Sites
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FactorNet: A deep learning framework for predicting cell type specific transcription factor binding from nucleotide-resolution sequential data.

Methods (San Diego, Calif.) Mar 26, 2019
Due to the large numbers of transcription factors (TFs) and cell types, querying binding profiles of all valid TF/cell type pairs is not experimentally feasible. To address this issue, we developed a convolutional-recurrent neural network model, call...
Binding Sites Chromatin Neural Networks, Computer Software Protein Binding Deep Learning Transcription Factors Nucleotides
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Elucidating the druggability of the human proteome with eFindSite.

Journal of computer-aided molecular design Mar 19, 2019
Identifying the viability of protein targets is one of the preliminary steps of drug discovery. Determining the ability of a protein to bind drugs in order to modulate its function, termed the druggability, requires a non-trivial amount of time and r...
Supervised Machine Learning Humans Serine Proteases Software 5-Aminolevulinate Synthetase Protein Binding Drug Design Binding Sites Proteome Drug Discovery Proteins
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Predicting protein-peptide interaction sites using distant protein complexes as structural templates.

Scientific reports Mar 12, 2019
Protein-peptide interactions play an important role in major cellular processes, and are associated with several human diseases. To understand and potentially regulate these cellular function and diseases it is important to know the molecular details...
Binding Sites Protein Interaction Mapping Proteins Amino Acid Sequence Datasets as Topic Software Protein Binding Peptides Computational Biology Humans Molecular Docking Simulation Cluster Analysis Machine Learning
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