AIMC Topic: Ligands

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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, ...
Machine Learning Aptamers, Nucleotide Models, Chemical Protein Binding Lipocalin-2 Computer Simulation Gene Library Drug Discovery Ligands
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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...
Aptamers, Nucleotide Sequence Analysis, RNA Models, Genetic Machine Learning Computer-Aided Design Ligands High-Throughput Nucleotide Sequencing Gene Expression Regulation Escherichia coli Structure-Activity Relationship Neural Networks, Computer Saccharomyces cerevisiae RNA, Catalytic Base Sequence Nucleic Acid Conformation
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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...
Machine Learning Glucagon-Like Peptide-1 Receptor Drug Discovery Ligands
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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...
Models, Theoretical Proteins Ligands Databases, Protein Neural Networks, Computer Protein Binding
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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 Proteins Ligands Drug Discovery Deep Learning 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 Dynamics Simulation Ligands Molecular Conformation Structure-Activity Relationship Molecular Structure Small Molecule Libraries Protein Interaction Mapping Kelch-Like ECH-Associated Protein 1 NF-E2-Related Factor 2 Binding Sites Machine Learning Molecular Docking Simulation Protein Interaction Maps Protein Binding Drug Discovery Humans
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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...
Deep Learning Ligands Machine Learning Computer Simulation
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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...
Humans Ligands Machine Learning Phylogeny Sequence Alignment Computational Biology Amino Acid Sequence
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Improved Protein-Ligand Binding Affinity Prediction with Structure-Based Deep Fusion Inference.

Journal of chemical information and modeling Mar 23, 2021
Predicting accurate protein-ligand binding affinities is an important task in drug discovery but remains a challenge even with computationally expensive biophysics-based energy scoring methods and state-of-the-art deep learning approaches. Despite th...
Proteins Ligands Software Protein Binding Neural Networks, Computer
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Ollivier Persistent Ricci Curvature-Based Machine Learning for the Protein-Ligand Binding Affinity Prediction.

Journal of chemical information and modeling Mar 16, 2021
Efficient molecular featurization is one of the major issues for machine learning models in drug design. Here, we propose a persistent Ricci curvature (PRC), in particular, Ollivier PRC (OPRC), for the molecular featurization and feature engineering,...
Protein Binding Proteins Ligands Databases, Protein Machine Learning
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