AIMC Topic: Ligands

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Improving Structure-Based Virtual Screening with Ensemble Docking and Machine Learning.

Journal of chemical information and modeling Oct 15, 2021
One of the main challenges of structure-based virtual screening (SBVS) is the incorporation of the receptor's flexibility, as its explicit representation in every docking run implies a high computational cost. Therefore, a common alternative to inclu...
Protein Binding Proteins Ligands Machine Learning Molecular Docking Simulation Protein Conformation
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Development of a proteochemometric-based support vector machine model for predicting bioactive molecules of tubulin receptors.

Molecular diversity Oct 9, 2021
Microtubules are receiving enormous interest in drug discovery due to the important roles they play in cellular functions. Targeting tubulin polymerization presents an excellent opportunity for the development of anti-tubulin drugs. Drug resistance a...
Tubulin Support Vector Machine Amino Acid Sequence Ligands Algorithms
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MANORAA: A machine learning platform to guide protein-ligand design by anchors and influential distances.

Structure (London, England : 1993) Oct 5, 2021
The MANORAA platform uses structure-based approaches to provide information on drug design originally derived from mapping tens of thousands of amino acids on a grid. In-depth analyses of the pockets, frequently occurring atoms, influential distances...
Computational Biology Proteins Databases, Protein Drug Design Protein Binding Crystallography, X-Ray Humans Trimethoprim Machine Learning Tetrahydrofolate Dehydrogenase Pandemics Models, Molecular COVID-19 Protein Domains SARS-CoV-2 Ligands
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Guided structure-based ligand identification and design via artificial intelligence modeling.

Expert opinion on drug discovery Sep 23, 2021
INTRODUCTION: The implementation of Artificial Intelligence (AI) methodologies to drug discovery (DD) are on the rise. Several applications have been developed for structure-based DD, where AI methods provide an alternative framework for the identifi...
Ligands Humans Artificial Intelligence Machine Learning Molecular Docking Simulation Drug Design
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FFENCODER-PL: Pair Wise Energy Descriptors for Protein-Ligand Pose Selection.

Journal of chemical theory and computation Sep 23, 2021
Scoring functions are the essential component in molecular docking methods. An accurate scoring function is expected to distinguish the native ligand pose from decoy poses. Our previous experience (Pei et al. 2019, 59 (7), 3305-3315) proved that com...
Protein Binding Proteins Ligands Algorithms Molecular Docking Simulation Machine Learning
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Machine-learning methods for ligand-protein molecular docking.

Drug discovery today Sep 21, 2021
Artificial intelligence (AI) is often presented as a new Industrial Revolution. Many domains use AI, including molecular simulation for drug discovery. In this review, we provide an overview of ligand-protein molecular docking and how machine learnin...
Machine Learning Molecular Docking Simulation Drug Discovery Deep Learning Ligands Humans Artificial Intelligence
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Learning from Docked Ligands: Ligand-Based Features Rescue Structure-Based Scoring Functions When Trained on Docked Poses.

Journal of chemical information and modeling Sep 1, 2021
Machine learning scoring functions for protein-ligand binding affinity have been found to consistently outperform classical scoring functions when trained and tested on crystal structures of bound protein-ligand complexes. However, it is less clear h...
Molecular Docking Simulation Protein Binding Proteins Machine Learning Ligands
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DeepPocket: Ligand Binding Site Detection and Segmentation using 3D Convolutional Neural Networks.

Journal of chemical information and modeling Aug 10, 2021
A structure-based drug design pipeline involves the development of potential drug molecules or ligands that form stable complexes with a given receptor at its binding site. A prerequisite to this is finding druggable and functionally relevant binding...
Neural Networks, Computer Binding Sites Algorithms Software Proteins Ligands
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A novel artificial intelligence-based approach for identification of deoxynucleotide aptamers.

PLoS computational biology Aug 3, 2021
The selection of a DNA aptamer through the Systematic Evolution of Ligands by EXponential enrichment (SELEX) method involves multiple binding steps, in which a target and a library of randomized DNA sequences are mixed for selection of a single, nucl...
Logistic Models Support Vector Machine Decision Trees Natural Language Processing Bayes Theorem Aptamers, Nucleotide Humans Protein Binding Machine Learning Artificial Intelligence Computational Biology SELEX Aptamer Technique Algorithms Ligands Gene Library
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Logistic matrix factorisation and generative adversarial neural network-based method for predicting drug-target interactions.

Molecular diversity Jul 23, 2021
Identifying drug-target protein association pairs is a prerequisite and a crucial task in drug discovery and development. Numerous computational models, based on different assumptions and algorithms, have been proposed as an alternative to the labori...
Binding Sites Drug Discovery Models, Molecular Algorithms Deep Learning Ligands Humans Protein Binding Databases, Factual Neural Networks, Computer Models, Theoretical Biomarkers Drug Development
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