AIMC Topic: Molecular Dynamics Simulation

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PeSTo: parameter-free geometric deep learning for accurate prediction of protein binding interfaces.

Nature communications Apr 18, 2023
Proteins are essential molecular building blocks of life, responsible for most biological functions as a result of their specific molecular interactions. However, predicting their  binding  interfaces remains a challenge. In this study, we present a ...
Proteins Molecular Dynamics Simulation Protein Binding Computational Biology Deep Learning
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Research and Evaluation of the Allosteric Protein-Specific Force Field Based on a Pre-Training Deep Learning Model.

Journal of chemical information and modeling Apr 14, 2023
Allosteric modulators are important regulation elements that bind the allosteric site beyond the active site, leading to the changes in dynamic and/or thermodynamic properties of the protein. Allosteric modulators have been a considerable interest as...
Molecular Dynamics Simulation Catalytic Domain Allosteric Regulation Allosteric Site Deep Learning Proteins
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Metastable alpha-rich and beta-rich conformations of small Aβ42 peptide oligomers.

Proteins Apr 10, 2023
Probing the structures of amyloid-β (Aβ) peptides in the early steps of aggregation is extremely difficult experimentally and computationally. Yet, this knowledge is extremely important as small oligomers are the most toxic species. Experiments and s...
Peptide Fragments Molecular Dynamics Simulation Protein Aggregates Machine Learning Amyloid beta-Peptides Protein Multimerization Protein Conformation, alpha-Helical Protein Conformation, beta-Strand Thermodynamics Protein Structure, Secondary Humans
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A deep learning and docking simulation-based virtual screening strategy enables the rapid identification of HIF-1α pathway activators from a marine natural product database.

Journal of biomolecular structure & dynamics Apr 10, 2023
Artificial Intelligence is hailed as a cutting-edge technology for accelerating drug discovery efforts, and our goal was to validate its potential in predicting pharmacological inhibitors of EGLN1 using a deep learning-based architecture, one of its ...
Deep Learning Biological Products Artificial Intelligence Molecular Docking Simulation Molecular Dynamics Simulation Humans
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Enhancing Conformational Sampling for Intrinsically Disordered and Ordered Proteins by Variational Autoencoder.

International journal of molecular sciences Apr 7, 2023
Intrinsically disordered proteins (IDPs) account for more than 50% of the human proteome and are closely associated with tumors, cardiovascular diseases, and neurodegeneration, which have no fixed three-dimensional structure under physiological condi...
Protein Conformation Magnetic Resonance Spectroscopy Artificial Intelligence Molecular Dynamics Simulation Intrinsically Disordered Proteins Humans
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Artificial intelligence assisted identification of potential tau aggregation inhibitors: ligand- and structure-based virtual screening, in silico ADME, and molecular dynamics study.

Molecular diversity Apr 6, 2023
Alzheimer's disease (AD) is a severe, growing, multifactorial disorder affecting millions of people worldwide characterized by cognitive decline and neurodegeneration. The accumulation of tau protein into paired helical filaments is one of the major ...
Artificial Intelligence Computer Simulation Molecular Docking Simulation Humans Alzheimer Disease Ligands Protein Aggregates tau Proteins Molecular Dynamics Simulation Drug Discovery Drug Evaluation, Preclinical Protein Binding
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Identification of medicinal plant-based phytochemicals as a potential inhibitor for SARS-CoV-2 main protease (M) using molecular docking and deep learning methods.

Computers in biology and medicine Mar 11, 2023
Highly transmissive and rapidly evolving Coronavirus disease-2019 (COVID-19), a viral disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), triggered a global pandemic, which is one of the most researched viruses in the acad...
SARS-CoV-2 Molecular Docking Simulation Molecular Dynamics Simulation Plants, Medicinal Deep Learning Humans COVID-19 Protease Inhibitors
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Uncertainty-driven dynamics for active learning of interatomic potentials.

Nature computational science Mar 6, 2023
Machine learning (ML) models, if trained to data sets of high-fidelity quantum simulations, produce accurate and efficient interatomic potentials. Active learning (AL) is a powerful tool to iteratively generate diverse data sets. In this approach, th...
Machine Learning Molecular Dynamics Simulation Glycine Uncertainty
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Protein Design Using Physics Informed Neural Networks.

Biomolecules Mar 1, 2023
The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function. Recent advancements in machine learning techniques have been su...
Amino Acid Sequence Neural Networks, Computer Physics Proteins Molecular Dynamics Simulation
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Machine learning combines atomistic simulations to predict SARS-CoV-2 Mpro inhibitors from natural compounds.

Molecular diversity Feb 24, 2023
To date, the COVID-19 pandemic has still been infectious around the world, continuously causing social and economic damage on a global scale. One of the most important therapeutic targets for the treatment of COVID-19 is the main protease (Mpro) of S...
Protease Inhibitors Coronavirus 3C Proteases Molecular Docking Simulation COVID-19 Humans Molecular Dynamics Simulation Antiviral Agents SARS-CoV-2 Biological Products COVID-19 Drug Treatment Machine Learning
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