AIMC Topic: Ligands

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CSConv2d: A 2-D Structural Convolution Neural Network with a Channel and Spatial Attention Mechanism for Protein-Ligand Binding Affinity Prediction.

Biomolecules Apr 27, 2021
The binding affinity of small molecules to receptor proteins is essential to drug discovery and drug repositioning. Chemical methods are often time-consuming and costly, and models for calculating the binding affinity are imperative. In this study, w...
Amino Acid Sequence Ligands Protein Binding Proteins Drug Repositioning Drug Discovery Deep Learning Neural Networks, Computer Forecasting
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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...
Ligands Deep Learning Proteins Reproducibility of Results
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Machine learning guided aptamer refinement and discovery.

Nature communications Apr 22, 2021
Aptamers are single-stranded nucleic acid ligands that bind to target molecules with high affinity and specificity. They are typically discovered by searching large libraries for sequences with desirable binding properties. These libraries, however, ...
Protein Binding Models, Chemical Ligands Gene Library Lipocalin-2 Computer Simulation Drug Discovery Aptamers, Nucleotide Machine Learning
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A convolutional neural network for the prediction and forward design of ribozyme-based gene-control elements.

eLife Apr 16, 2021
Ribozyme switches are a class of RNA-encoded genetic switch that support conditional regulation of gene expression across diverse organisms. An improved elucidation of the relationships between sequence, structure, and activity can improve our capaci...
Computer-Aided Design Aptamers, Nucleotide Sequence Analysis, RNA Neural Networks, Computer Machine Learning Structure-Activity Relationship Ligands High-Throughput Nucleotide Sequencing Models, Genetic Escherichia coli Nucleic Acid Conformation Saccharomyces cerevisiae RNA, Catalytic Gene Expression Regulation Base Sequence
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Ligand-Receptor Interactions and Machine Learning in GCGR and GLP-1R Drug Discovery.

International journal of molecular sciences Apr 14, 2021
The large amount of data that has been collected so far for G protein-coupled receptors requires machine learning (ML) approaches to fully exploit its potential. Our previous ML model based on gradient boosting used for prediction of drug affinity an...
Drug Discovery Ligands Machine Learning Glucagon-Like Peptide-1 Receptor
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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...
Databases, Protein Ligands Protein Binding Models, Theoretical Proteins Neural Networks, Computer
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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...
Proteins Ligands Drug Discovery Deep Learning Protein Binding Neural Networks, Computer Molecular Docking Simulation
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Identification of novel inhibitors of Keap1/Nrf2 by a promising method combining protein-protein interaction-oriented library and machine learning.

Scientific reports Apr 1, 2021
Protein-protein interactions (PPIs) are prospective but challenging targets for drug discovery, because screening using traditional small-molecule libraries often fails to identify hits. Recently, we developed a PPI-oriented library comprising 12,593...
Molecular Structure Molecular Dynamics Simulation Ligands Protein Binding Structure-Activity Relationship Small Molecule Libraries Protein Interaction Mapping Kelch-Like ECH-Associated Protein 1 NF-E2-Related Factor 2 Binding Sites Molecular Conformation Humans Machine Learning Drug Discovery Protein Interaction Maps Molecular Docking Simulation
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Balancing Data on Deep Learning-Based Proteochemometric Activity Classification.

Journal of chemical information and modeling Mar 29, 2021
In silico analysis of biological activity data has become an essential technique in pharmaceutical development. Specifically, the so-called proteochemometric models aim to share information between targets in machine learning ligand-target activity p...
Ligands Computer Simulation Deep Learning Machine Learning
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MSA-Regularized Protein Sequence Transformer toward Predicting Genome-Wide Chemical-Protein Interactions: Application to GPCRome Deorphanization.

Journal of chemical information and modeling Mar 23, 2021
Small molecules play a critical role in modulating biological systems. Knowledge of chemical-protein interactions helps address fundamental and practical questions in biology and medicine. However, with the rapid emergence of newly sequenced genes, t...
Machine Learning Phylogeny Computational Biology Humans Sequence Alignment Amino Acid Sequence Ligands
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