AIMC Topic: Ligands

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SCORCH: Improving structure-based virtual screening with machine learning classifiers, data augmentation, and uncertainty estimation.

Journal of advanced research Jul 25, 2022
INTRODUCTION: The discovery of a new drug is a costly and lengthy endeavour. The computational prediction of which small molecules can bind to a protein target can accelerate this process if the predictions are fast and accurate enough. Recent machin...
Machine Learning Molecular Docking Simulation Protein Binding Uncertainty Proteins Ligands
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Prediction of protein mononucleotide binding sites using AlphaFold2 and machine learning.

Computational biology and chemistry Jul 23, 2022
In this study, we developed a system that predicts the binding sites of proteins for five mononucleotides (AMP, ADP, ATP, GDP, and GTP). The system comprises two machine learning (ML)-based predictors using a convolutional neural network and a gradie...
Adenosine Monophosphate Binding Sites Adenosine Diphosphate Adenosine Triphosphate Ligands Protein Binding Proteins Machine Learning
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DeepBindBC: A practical deep learning method for identifying native-like protein-ligand complexes in virtual screening.

Methods (San Diego, Calif.) Jul 22, 2022
Identifying native-like protein-ligand complexes (PLCs) from an abundance of docking decoys is critical for large-scale virtual drug screening in early-stage drug discovery lead searching efforts. Providing reliable prediction is still a challenge fo...
Proteins Molecular Dynamics Simulation Ligands Deep Learning Protein Binding Humans Molecular Docking Simulation
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Protein-Ligand Docking in the Machine-Learning Era.

Molecules (Basel, Switzerland) Jul 18, 2022
Molecular docking plays a significant role in early-stage drug discovery, from structure-based virtual screening (VS) to hit-to-lead optimization, and its capability and predictive power is critically dependent on the protein-ligand scoring function....
Molecular Docking Simulation Protein Binding Proteins Ligands Machine Learning
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Informed Chemical Classification of Organophosphorus Compounds via Unsupervised Machine Learning of X-ray Absorption Spectroscopy and X-ray Emission Spectroscopy.

The journal of physical chemistry. A Jul 15, 2022
We analyze an ensemble of organophosphorus compounds to form an unbiased characterization of the information encoded in their X-ray absorption near-edge structure (XANES) and valence-to-core X-ray emission spectra (VtC-XES). Data-driven emergence of ...
Humans X-Ray Absorption Spectroscopy Organophosphorus Compounds Spectrometry, X-Ray Emission Unsupervised Machine Learning Ligands
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Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.

Molecular diversity Jul 11, 2022
CNS disorders are indications with a very high unmet medical needs, relatively smaller number of available drugs, and a subpar satisfaction level among patients and caregiver. Discovery of CNS drugs is extremely expensive affair with its own unique c...
Central Nervous System Proteomics Ligands Humans Artificial Intelligence Machine Learning
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Molecule Design Using Molecular Generative Models Constrained by Ligand-Protein Interactions.

Journal of chemical information and modeling Jul 6, 2022
In recent years, molecular deep generative models have attracted much attention for its application in drug design. The data-driven molecular deep generative model approximates the high dimensional distribution of the chemical space through learning...
Neural Networks, Computer Models, Molecular Ligands Drug Design Proteins
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AI-based prediction of new binding site and virtual screening for the discovery of novel P2X3 receptor antagonists.

European journal of medicinal chemistry Jul 1, 2022
Artificial intelligence (AI) has been recognized as a powerful technique that can accelerate drug discovery during the hit compound identification step. However, most simple deep learning models have been used for naive pre-filtering as the predictio...
Protein Binding Molecular Dynamics Simulation Molecular Docking Simulation Drug Discovery Purinergic P2X Receptor Antagonists Ligands Binding Sites Artificial Intelligence
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A Physics-Guided Neural Network for Predicting Protein-Ligand Binding Free Energy: From Host-Guest Systems to the PDBbind Database.

Biomolecules Jun 29, 2022
Calculation of protein-ligand binding affinity is a cornerstone of drug discovery. Classic implicit solvent models, which have been widely used to accomplish this task, lack accuracy compared to experimental references. Emerging data-driven models, o...
Ligands Solvents Protein Binding Neural Networks, Computer Thermodynamics Proteins Physics
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Predicting Protein-Ligand Docking Structure with Graph Neural Network.

Journal of chemical information and modeling Jun 14, 2022
Modern day drug discovery is extremely expensive and time consuming. Although computational approaches help accelerate and decrease the cost of drug discovery, existing computational software packages for docking-based drug discovery suffer from both...
Protein Binding Ligands Proteins Neural Networks, Computer Molecular Docking Simulation
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