AIMC Topic: Proteins

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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...
Ligands Polypharmacology Drug Design Drug Discovery Proteins Models, Chemical Deep Learning Humans
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Utilizing Molecular Dynamics Simulations, Machine Learning, Cryo-EM, and NMR Spectroscopy to Predict and Validate Protein Dynamics.

International journal of molecular sciences Sep 8, 2024
Protein dynamics play a crucial role in biological function, encompassing motions ranging from atomic vibrations to large-scale conformational changes. Recent advancements in experimental techniques, computational methods, and artificial intelligence...
Cryoelectron Microscopy Nuclear Magnetic Resonance, Biomolecular Magnetic Resonance Spectroscopy Humans Machine Learning Proteins Molecular Dynamics Simulation Protein Conformation
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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...
Machine Learning Software Proteins Ligands Protein Conformation Molecular Docking Simulation Protein Binding
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Artificial Intelligence Learns Protein Prediction.

Cold Spring Harbor perspectives in biology Sep 3, 2024
From over to , the recent decade of exponential advances in artificial intelligence (AI) has been altering life. In parallel, advances in computational biology are beginning to decode the language of life: leaped forward in protein structure predi...
Computational Biology Proteins Databases, Protein Artificial Intelligence
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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...
Protein Binding Proteins Neural Networks, Computer Drug Discovery Deep Learning Ligands
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Enhancing Missense Variant Pathogenicity Prediction with MissenseNet: Integrating Structural Insights and ShuffleNet-Based Deep Learning Techniques.

Biomolecules Sep 2, 2024
The classification of missense variant pathogenicity continues to pose significant challenges in human genetics, necessitating precise predictions of functional impacts for effective disease diagnosis and personalized treatment strategies. Traditiona...
Humans Deep Learning Proteins ROC Curve Computational Biology Mutation, Missense
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PCP-GC-LM: single-sequence-based protein contact prediction using dual graph convolutional neural network and convolutional neural network.

BMC bioinformatics Sep 2, 2024
BACKGROUND: Recently, the process of evolution information and the deep learning network has promoted the improvement of protein contact prediction methods. Nevertheless, still remain some bottleneck: (1) One of the bottlenecks is the prediction of o...
Proteins Databases, Protein Sequence Analysis, Protein Protein Conformation Neural Networks, Computer Deep Learning Algorithms Computational Biology
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Improvement of bioanalytical parameters through automation: suitability of a hand-like robotic system.

Analytical and bioanalytical chemistry Aug 29, 2024
Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot's movements but also for adapting the...
Limit of Detection Automation Proteins Humans Robotics Automation, Laboratory
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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...
Molecular Dynamics Simulation Ligands Protein Binding Proteins Thermodynamics HSP90 Heat-Shock Proteins Drug Design Deep Learning
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Predicting the Binding of Small Molecules to Proteins through Invariant Representation of the Molecular Structure.

Journal of chemical information and modeling Aug 28, 2024
We present a computational scheme for predicting the ligands that bind to a pocket of a known structure. It is based on the generation of a general abstract representation of the molecules, which is invariant to rotations, translations, and permutati...
Protein Conformation Binding Sites Machine Learning Small Molecule Libraries Algorithms Proteins Molecular Structure Protein Binding Ligands Models, Molecular
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