AIMC Topic: Ligands

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InteractionGraphNet: A Novel and Efficient Deep Graph Representation Learning Framework for Accurate Protein-Ligand Interaction Predictions.

Journal of medicinal chemistry Dec 8, 2021
Accurate quantification of protein-ligand interactions remains a key challenge to structure-based drug design. However, traditional machine learning (ML)-based methods based on handcrafted descriptors, one-dimensional protein sequences, and/or two-di...
Protein Binding Proteins Ligands Algorithms Deep Learning
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LCP: Simple Representation of Docking Poses for Machine Learning: A Case Study on Xanthine Oxidase Inhibitors.

Molecular informatics Nov 29, 2021
In this paper, we propose a simple descriptor called the ligand coordinate profile (LCP) for describing docking poses. The LCP descriptor is generated from the coordinates of the polar hydrogen and heavy atoms of the docked ligand. We hypothesize tha...
Ligands Enzyme Inhibitors Molecular Docking Simulation Protein Binding Xanthine Oxidase Machine Learning
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Unsupervised Representation Learning for Proteochemometric Modeling.

International journal of molecular sciences Nov 28, 2021
In silico protein-ligand binding prediction is an ongoing area of research in computational chemistry and machine learning based drug discovery, as an accurate predictive model could greatly reduce the time and resources necessary for the detection a...
Cheminformatics Proteins Unsupervised Machine Learning Ligands Quantitative Structure-Activity Relationship
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Ligand-Based Virtual Screening Based on the Graph Edit Distance.

International journal of molecular sciences Nov 25, 2021
Chemical compounds can be represented as attributed graphs. An attributed graph is a mathematical model of an object composed of two types of representations: nodes and edges. Nodes are individual components, and edges are relations between these com...
Models, Theoretical Ligands Computer Graphics Algorithms User-Computer Interface Artificial Intelligence
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PremPLI: a machine learning model for predicting the effects of missense mutations on protein-ligand interactions.

Communications biology Nov 19, 2021
Resistance to small-molecule drugs is the main cause of the failure of therapeutic drugs in clinical practice. Missense mutations altering the binding of ligands to proteins are one of the critical mechanisms that result in genetic disease and drug r...
Protein Binding Proteins Machine Learning Ligands Mutation, Missense
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Structure-Based Drug Design Using Deep Learning.

Journal of chemical information and modeling Nov 18, 2021
In recent years, deep learning-based methods have emerged as promising tools for drug design. Most of these methods are ligand-based, where an initial target-specific ligand data set is necessary to design potent molecules with optimized properties....
Drug Design Proteins Ligands Deep Learning Models, Molecular
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Binding affinity prediction for protein-ligand complex using deep attention mechanism based on intermolecular interactions.

BMC bioinformatics Nov 8, 2021
BACKGROUND: Accurate prediction of protein-ligand binding affinity is important for lowering the overall cost of drug discovery in structure-based drug design. For accurate predictions, many classical scoring functions and machine learning-based meth...
Ligands Machine Learning Binding Sites Protein Binding Proteins
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End-to-end learning for compound activity prediction based on binding pocket information.

BMC bioinformatics Oct 29, 2021
BACKGROUND: Recently, machine learning-based ligand activity prediction methods have been greatly improved. However, if known active compounds of a target protein are unavailable, the machine learning-based method cannot be applied. In such cases, do...
Amino Acid Sequence Ligands Protein Binding Proteins Machine Learning Molecular Docking Simulation Neural Networks, Computer
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Using diverse potentials and scoring functions for the development of improved machine-learned models for protein-ligand affinity and docking pose prediction.

Journal of computer-aided molecular design Oct 28, 2021
The advent of computational drug discovery holds the promise of significantly reducing the effort of experimentalists, along with monetary cost. More generally, predicting the binding of small organic molecules to biological macromolecules has far-re...
Machine Learning Molecular Docking Simulation Drug Discovery Proteins Ligands Thermodynamics
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Machine Learning Assisted Approach for Finding Novel High Activity Agonists of Human Ectopic Olfactory Receptors.

International journal of molecular sciences Oct 26, 2021
Olfactory receptors (ORs) constitute the largest superfamily of G protein-coupled receptors (GPCRs). ORs are involved in sensing odorants as well as in other ectopic roles in non-nasal tissues. Matching of an enormous number of the olfactory stimulat...
Bayes Theorem Receptors, Odorant Molecular Docking Simulation Drug Evaluation, Preclinical Ligands Drug Design User-Computer Interface HEK293 Cells Drug Discovery Support Vector Machine Female In Vitro Techniques Male Humans Machine Learning
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