AIMC Topic: Molecular Dynamics Simulation

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Determination of Novel SARS-CoV-2 Inhibitors by Combination of Machine Learning and Molecular Modeling Methods.

Medicinal chemistry (Shariqah (United Arab Emirates)) Jan 1, 2024
INTRODUCTION: Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the ...
Amides Pyrazines Molecular Docking Simulation Antiviral Agents Peptide Hydrolases RNA-Dependent RNA Polymerase Molecular Dynamics Simulation Protease Inhibitors Artificial Intelligence Machine Learning Humans COVID-19 SARS-CoV-2
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Unsupervised and supervised AI on molecular dynamics simulations reveals complex characteristics of HLA-A2-peptide immunogenicity.

Briefings in bioinformatics Nov 22, 2023
Immunologic recognition of peptide antigens bound to class I major histocompatibility complex (MHC) molecules is essential to both novel immunotherapeutic development and human health at large. Current methods for predicting antigen peptide immunogen...
Protein Binding Artificial Intelligence Molecular Dynamics Simulation Histocompatibility Antigens Class I Humans Peptides HLA-A2 Antigen
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Unraveling viral drug targets: a deep learning-based approach for the identification of potential binding sites.

Briefings in bioinformatics Nov 22, 2023
The coronavirus disease 2019 (COVID-19) pandemic has spurred a wide range of approaches to control and combat the disease. However, selecting an effective antiviral drug target remains a time-consuming challenge. Computational methods offer a promisi...
Humans COVID-19 Protein Binding Antibodies, Viral SARS-CoV-2 Deep Learning Binding Sites Spike Glycoprotein, Coronavirus Molecular Dynamics Simulation
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Toward a general neural network force field for protein simulations: Refining the intramolecular interaction in protein.

The Journal of chemical physics Jul 14, 2023
Molecular dynamics (MD) is an extremely powerful, highly effective, and widely used approach to understanding the nature of chemical processes in atomic details for proteins. The accuracy of results from MD simulations is highly dependent on force fi...
Molecular Dynamics Simulation Dipeptides Neural Networks, Computer Machine Learning Water
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A machine learning potential for simulating infrared spectra of nanosilicate clusters.

The Journal of chemical physics Jun 14, 2023
The use of machine learning (ML) in chemical physics has enabled the construction of interatomic potentials having the accuracy of ab initio methods and a computational cost comparable to that of classical force fields. Training an ML model requires ...
Light Machine Learning Molecular Dynamics Simulation Neural Networks, Computer
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Predicting residue cooperativity during protein folding: A combined, molecular dynamics and unsupervised learning approach.

The Journal of chemical physics Apr 7, 2023
Allostery in proteins involves, broadly speaking, ligand-induced conformational transitions that modulate function at active sites distal to where the ligand binds. In contrast, the concept of cooperativity (in the sense used in phase transition theo...
Protein Folding Proteins Unsupervised Machine Learning Molecular Dynamics Simulation Ligands
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Designing antimicrobial peptides using deep learning and molecular dynamic simulations.

Briefings in bioinformatics Mar 19, 2023
With the emergence of multidrug-resistant bacteria, antimicrobial peptides (AMPs) offer promising options for replacing traditional antibiotics to treat bacterial infections, but discovering and designing AMPs using traditional methods is a time-cons...
Anti-Bacterial Agents Gram-Positive Bacteria Deep Learning Molecular Dynamics Simulation Antimicrobial Peptides Antimicrobial Cationic Peptides Gram-Negative Bacteria
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Binary salt structure classification with convolutional neural networks: Application to crystal nucleation and melting point calculations.

The Journal of chemical physics Nov 28, 2022
Convolutional neural networks are constructed and validated for the crystal structure classification of simple binary salts such as the alkali halides. The inputs of the neural network classifiers are the local bond orientational order parameters of ...
Alkalies Sodium Chloride Neural Networks, Computer Crystallization Molecular Dynamics Simulation
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ComABAN: refining molecular representation with the graph attention mechanism to accelerate drug discovery.

Briefings in bioinformatics Sep 20, 2022
An unsolved challenge in developing molecular representation is determining an optimal method to characterize the molecular structure. Comprehension of intramolecular interactions is paramount toward achieving this goal. In this study, ComABAN, a new...
Neural Networks, Computer Molecular Structure Molecular Dynamics Simulation Machine Learning Drug Discovery
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Ab initio machine learning of phase space averages.

The Journal of chemical physics Jul 14, 2022
Equilibrium structures determine material properties and biochemical functions. We here propose to machine learn phase space averages, conventionally obtained by ab initio or force-field-based molecular dynamics (MD) or Monte Carlo (MC) simulations. ...
Monte Carlo Method Machine Learning Molecular Dynamics Simulation
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