AIMC Topic: Ligands

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Comparative analysis of RNA 3D structure prediction methods: towards enhanced modeling of RNA-ligand interactions.

Nucleic acids research Jul 22, 2024
Accurate RNA structure models are crucial for designing small molecule ligands that modulate their functions. This study assesses six standalone RNA 3D structure prediction methods-DeepFoldRNA, RhoFold, BRiQ, FARFAR2, SimRNA and Vfold2, excluding web...
Software Models, Molecular Machine Learning Nucleic Acid Conformation Binding Sites RNA Folding RNA Ligands
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DEAttentionDTA: protein-ligand binding affinity prediction based on dynamic embedding and self-attention.

Bioinformatics (Oxford, England) Jun 3, 2024
MOTIVATION: Predicting protein-ligand binding affinity is crucial in new drug discovery and development. However, most existing models rely on acquiring 3D structures of elusive proteins. Combining amino acid sequences with ligand sequences and bette...
Neural Networks, Computer Software Protein Binding Ligands Binding Sites Databases, Protein Amino Acid Sequence Computational Biology Proteins
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OLB-AC: toward optimizing ligand bioactivities through deep graph learning and activity cliffs.

Bioinformatics (Oxford, England) Jun 3, 2024
MOTIVATION: Deep graph learning (DGL) has been widely employed in the realm of ligand-based virtual screening. Within this field, a key hurdle is the existence of activity cliffs (ACs), where minor chemical alterations can lead to significant changes...
Ligands Drug Discovery Deep Learning Neural Networks, Computer
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GEMF: a novel geometry-enhanced mid-fusion network for PLA prediction.

Briefings in bioinformatics May 23, 2024
Accurate prediction of protein-ligand binding affinity (PLA) is important for drug discovery. Recent advances in applying graph neural networks have shown great potential for PLA prediction. However, existing methods usually neglect the geometric inf...
Protein Binding Computational Biology Algorithms Drug Discovery Proteins Ligands Neural Networks, Computer
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RmsdXNA: RMSD prediction of nucleic acid-ligand docking poses using machine-learning method.

Briefings in bioinformatics Mar 27, 2024
Small molecule drugs can be used to target nucleic acids (NA) to regulate biological processes. Computational modeling methods, such as molecular docking or scoring functions, are commonly employed to facilitate drug design. However, the accuracy of ...
Ligands Nucleic Acids Molecular Docking Simulation Molecular Dynamics Simulation Machine Learning
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A new paradigm for applying deep learning to protein-ligand interaction prediction.

Briefings in bioinformatics Mar 27, 2024
Protein-ligand interaction prediction presents a significant challenge in drug design. Numerous machine learning and deep learning (DL) models have been developed to accurately identify docking poses of ligands and active compounds against specific t...
Protein Binding Proteins Ligands Drug Design Deep Learning
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Prediction of protein-ligand binding affinity via deep learning models.

Briefings in bioinformatics Jan 22, 2024
Accurately predicting the binding affinity between proteins and ligands is crucial in drug screening and optimization, but it is still a challenge in computer-aided drug design. The recent success of AlphaFold2 in predicting protein structures has br...
Drug Evaluation, Preclinical Ligands Drug Design Deep Learning Databases, Factual
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ASD2023: towards the integrating landscapes of allosteric knowledgebase.

Nucleic acids research Jan 5, 2024
Allosteric regulation, induced by perturbations at an allosteric site topographically distinct from the orthosteric site, is one of the most direct and efficient ways to fine-tune macromolecular function. The Allosteric Database (ASD; accessible onli...
Humans Allosteric Site Drug Discovery Allosteric Regulation Knowledge Bases Protein Interaction Maps Ligands Proteome
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Estimating protein-ligand interactions with geometric deep learning and mixture density models.

Journal of biosciences Jan 1, 2024
Understanding the interactions between a ligand and its molecular target is crucial in guiding the optimization of molecules for any drug design workflow. Multiple experimental and computational methods have been developed to better understand these...
Protein Conformation Deep Learning Protein Binding Binding Sites Proteins Neural Networks, Computer Molecular Docking Simulation Ligands Algorithms Drug Design
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Advances in Protein-Ligand Binding Affinity Prediction via Deep Learning: A Comprehensive Study of Datasets, Data Preprocessing Techniques, and Model Architectures.

Current drug targets Jan 1, 2024
BACKGROUND: Drug discovery is a complex and expensive procedure involving several timely and costly phases through which new potential pharmaceutical compounds must pass to get approved. One of these critical steps is the identification and optimizat...
Ligands Humans Neural Networks, Computer Protein Binding Computational Biology Proteins Drug Discovery Deep Learning
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