AIMC Topic: Ligands

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Prediction of aptamer affinity using an artificial intelligence approach.

Journal of materials chemistry. B Sep 18, 2024
Aptamers are oligonucleotide sequences that can connect to particular target molecules, similar to monoclonal antibodies. They can be chosen by systematic evolution of ligands by exponential enrichment (SELEX), and are modifiable and can be synthesiz...
Humans Aptamers, Nucleotide Artificial Intelligence Molecular Dynamics Simulation Molecular Docking Simulation Ligands
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Combined Physics- and Machine-Learning-Based Method to Identify Druggable Binding Sites Using SILCS-Hotspots.

Journal of chemical information and modeling Sep 16, 2024
Identifying druggable binding sites on proteins is an important and challenging problem, particularly for cryptic, allosteric binding sites that may not be obvious from X-ray, cryo-EM, or predicted structures. The Site-Identification by Ligand Compet...
Protein Conformation Machine Learning Proteins Binding Sites Molecular Dynamics Simulation Drug Discovery Ligands Protein Binding
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Automated design of multi-target ligands by generative deep learning.

Nature communications Sep 11, 2024
Generative deep learning models enable data-driven de novo design of molecules with tailored features. Chemical language models (CLM) trained on string representations of molecules such as SMILES have been successfully employed to design new chemical...
Models, Chemical Ligands Polypharmacology Drug Discovery Humans Deep Learning Drug Design Proteins
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Linear symmetric self-selecting 14-bit kinetic molecular memristors.

Nature Sep 11, 2024
Artificial Intelligence (AI) is the domain of large resource-intensive data centres that limit access to a small community of developers. Neuromorphic hardware promises greatly improved space and energy efficiency for AI but is presently only capable...
Kinetics Ligands Thermodynamics Fourier Analysis Signal-To-Noise Ratio Neural Networks, Computer Artificial Intelligence Signal Processing, Computer-Assisted
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In silico assessments of the small molecular boron agents to pave the way for artificial intelligence-based boron neutron capture therapy.

European journal of medicinal chemistry Sep 6, 2024
Boron neutron capture therapy (BNCT) is a highly targeted, selective and effective technique to cure various types of cancers, with less harm to the healthy cells. In principle, BNCT treatment needs to distribute the boron (B) atoms inside the tumor ...
Ligands Small Molecule Libraries Boron Neutron Capture Therapy Boron Boron Compounds Humans Molecular Docking Simulation Artificial Intelligence Computer Simulation Molecular Structure Neoplasms Molecular Dynamics Simulation
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Teaching old docks new tricks with machine learning enhanced ensemble docking.

Scientific reports Sep 5, 2024
We here introduce Ensemble Optimizer (EnOpt), a machine-learning tool to improve the accuracy and interpretability of ensemble virtual screening (VS). Ensemble VS is an established method for predicting protein/small-molecule (ligand) binding. Unlike...
Molecular Docking Simulation Protein Binding Proteins Machine Learning Software Ligands Protein Conformation
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An artificial intelligence accelerated virtual screening platform for drug discovery.

Nature communications Sep 5, 2024
Structure-based virtual screening is a key tool in early drug discovery, with growing interest in the screening of multi-billion chemical compound libraries. However, the success of virtual screening crucially depends on the accuracy of the binding p...
Humans Artificial Intelligence Molecular Docking Simulation Ubiquitin-Protein Ligases Small Molecule Libraries Drug Evaluation, Preclinical NAV1.7 Voltage-Gated Sodium Channel Drug Discovery Ligands Crystallography, X-Ray Protein Binding
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Enhancing protein-ligand binding affinity prediction through sequential fusion of graph and convolutional neural networks.

Journal of computational chemistry Sep 2, 2024
Predicting protein-ligand binding affinity is a crucial and challenging task in structure-based drug discovery. With the accumulation of complex structures and binding affinity data, various machine-learning scoring functions, particularly those base...
Proteins Ligands Deep Learning Protein Binding Neural Networks, Computer Drug Discovery
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From Static to Dynamic Structures: Improving Binding Affinity Prediction with Graph-Based Deep Learning.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Aug 29, 2024
Accurate prediction of protein-ligand binding affinities is an essential challenge in structure-based drug design. Despite recent advances in data-driven methods for affinity prediction, their accuracy is still limited, partially because they only ta...
Protein Binding Drug Design Proteins HSP90 Heat-Shock Proteins Deep Learning Molecular Dynamics Simulation Ligands Thermodynamics
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Conformational Space Profiling Enhances Generic Molecular Representation for AI-Powered Ligand-Based Drug Discovery.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Aug 29, 2024
The molecular representation model is a neural network that converts molecular representations (SMILES, Graph) into feature vectors, and is an essential module applied across a wide range of artificial intelligence-driven drug discovery scenarios. Ho...
Neural Networks, Computer Ligands Drug Discovery Artificial Intelligence Molecular Conformation Models, Molecular
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