AIMC Topic: Binding Sites

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Significance of Data Selection in Deep Learning for Reliable Binding Mode Prediction of Ligands in the Active Site of CYP3A4.

Chemical & pharmaceutical bulletin Aug 17, 2019
For rational drug design, it is essential to predict the binding mode of protein-ligand complexes. Although various machine learning-based models have been reported that use convolutional neural networks (deep learning) to predict binding modes from ...
Humans Binding Sites Databases, Factual Deep Learning Ligands Cytochrome P-450 CYP3A
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Artificial Intelligence Approach To Investigate the Longevity Drug.

The journal of physical chemistry letters Aug 14, 2019
Longevity is a very important and interesting topic, and has been demonstrated to be related to longevity. We combined network pharmacology, machine learning, deep learning, and molecular dynamics (MD) simulation to investigate potent lead drugs. Re...
Binding Sites Receptor, IGF Type 1 Molecular Docking Simulation Thermodynamics Antigens, CD Signal Transduction Artificial Intelligence Drug Discovery Receptor, Insulin Ligands Algorithms Medicine, Chinese Traditional Protein Interaction Maps Inhibitory Concentration 50 Databases, Factual Protein Binding Klotho Proteins Glucuronidase
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BindSpace decodes transcription factor binding signals by large-scale sequence embedding.

Nature methods Aug 12, 2019
The decoding of transcription factor (TF) binding signals in genomic DNA is a fundamental problem. Here we present a prediction model called BindSpace that learns to embed DNA sequences and TF labels into the same space. By training on binding data f...
Humans Transcription Factors Machine Learning DNA Algorithms Binding Sites Protein Binding Chromatin Immunoprecipitation Computational Biology
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Artificial Intelligence Approach to Find Lead Compounds for Treating Tumors.

The journal of physical chemistry letters Jul 22, 2019
It has been demonstrated that MMP13 enzyme is related to most cancer cell tumors. The world's largest traditional Chinese medicine database was applied to screen for structure-based drug design and ligand-based drug design. To predict drug activity, ...
Cyclin-Dependent Kinase 6 Databases, Protein Machine Learning Databases, Pharmaceutical Models, Molecular Matrix Metalloproteinase 13 Mutation Ligands Antineoplastic Agents Proto-Oncogene Proteins c-bcl-2 Algorithms Deep Learning Tumor Suppressor Protein p53 Peptides Cyclin-Dependent Kinase Inhibitor p16 Binding Sites Drug Design Proteins Proto-Oncogene Proteins c-mdm2
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Prediction of ATP-binding sites in membrane proteins using a two-dimensional convolutional neural network.

Journal of molecular graphics & modelling Jul 15, 2019
Membrane proteins, the most important drug targets, account for around 30% of total proteins encoded by the genome of living organisms. An important role of these proteins is to bind adenosine triphosphate (ATP), facilitating crucial biological proce...
Algorithms Reproducibility of Results ROC Curve Computational Biology Membrane Proteins Models, Theoretical Databases, Protein Amino Acid Sequence Neural Networks, Computer Machine Learning Position-Specific Scoring Matrices Adenosine Triphosphate Binding Sites Amino Acid Motifs Web Browser
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Cross-Cell-Type Prediction of TF-Binding Site by Integrating Convolutional Neural Network and Adversarial Network.

International journal of molecular sciences Jul 12, 2019
Transcription factor binding sites (TFBSs) play an important role in gene expression regulation. Many computational methods for TFBS prediction need sufficient labeled data. However, many transcription factors (TFs) lack labeled data in cell types. W...
ROC Curve Protein Binding Computational Biology Transcription Factors Neural Networks, Computer Gene Expression Regulation Algorithms Organ Specificity Deep Learning Binding Sites
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Enabling full-length evolutionary profiles based deep convolutional neural network for predicting DNA-binding proteins from sequence.

Proteins Jul 8, 2019
Sequence based DNA-binding protein (DBP) prediction is a widely studied biological problem. Sliding windows on position specific substitution matrices (PSSMs) rows predict DNA-binding residues well on known DBPs but the same models cannot be applied ...
Neural Networks, Computer Animals Mice DNA Humans Binding Sites Arabidopsis Proteins Arabidopsis Amino Acids DNA-Binding Proteins Models, Biological Protein Conformation
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Coupling dynamics and evolutionary information with structure to identify protein regulatory and functional binding sites.

Proteins Jun 22, 2019
Binding sites in proteins can be either specifically functional binding sites (active sites) that bind specific substrates with high affinity or regulatory binding sites (allosteric sites), that modulate the activity of functional binding sites throu...
Allosteric Site Allosteric Regulation Molecular Dynamics Simulation Protein Conformation Binding Sites Protein Binding Evolution, Molecular Proteins Humans Databases, Protein Artificial Intelligence Machine Learning Algorithms
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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...
Computational Biology Computer Simulation Ligands Proteins Binding Sites Drug Discovery Models, Molecular Sequence Analysis, Protein Deep Learning Amino Acid Sequence
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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 Quantitative Structure-Activity Relationship Binding Sites Humans Drug Discovery Machine Learning Protein Binding Algorithms Receptors, G-Protein-Coupled Molecular Docking Simulation Molecular Dynamics Simulation Drug Design
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