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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...
Models, Genetic Escherichia coli Aptamers, Nucleotide Sequence Analysis, RNA High-Throughput Nucleotide Sequencing Gene Expression Regulation Base Sequence Structure-Activity Relationship Ligands Machine Learning Computer-Aided Design Neural Networks, Computer RNA, Catalytic Nucleic Acid Conformation Saccharomyces cerevisiae
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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 Drug Discovery Ligands 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 Models, Theoretical Proteins 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 Ligands Neural Networks, Computer Drug Discovery Proteins Deep Learning 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 Machine Learning Structure-Activity Relationship Molecular Docking Simulation Molecular Structure Protein Interaction Maps Small Molecule Libraries Protein Binding Protein Interaction Mapping Drug Discovery Kelch-Like ECH-Associated Protein 1 NF-E2-Related Factor 2 Humans Binding Sites
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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 Machine Learning Ligands 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...
Phylogeny Ligands Amino Acid Sequence Humans Computational Biology Sequence Alignment Machine Learning
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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...
Ligands Protein Binding Proteins Neural Networks, Computer Software
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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,...
Machine Learning Proteins Databases, Protein Protein Binding Ligands
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Target2DeNovoDrug: a novel programmatic tool for -deep learning based drug design for any target of interest.

Journal of biomolecular structure & dynamics Mar 11, 2021
The on-going data-science and Artificial Intelligence (AI) revolution offer researchers a fresh set of tools to approach structure-based drug design problems in the computer-aided drug design space. A novel programmatic tool that incorporates and de...
Ligands Drug Design Deep Learning Molecular Dynamics Simulation Artificial Intelligence
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