AIMC Topic: Molecular Dynamics Simulation

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An Unsupervised Machine Learning Approach for the Automatic Construction of Local Chemical Descriptors.

Journal of chemical information and modeling Mar 18, 2024
Condensing the many physical variables defining a chemical system into a fixed-size array poses a significant challenge in the development of chemical Machine Learning (ML). Atom Centered Symmetry Functions (ACSFs) offer an intuitive featurization ap...
Automation Normal Distribution Unsupervised Machine Learning Molecular Dynamics Simulation
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Understanding cellulose pyrolysis via ab initio deep learning potential field.

Bioresource technology Mar 13, 2024
Comprehensive and dynamic studies of cellulose pyrolysis reaction mechanisms are crucial in designing experiments and processes with enhanced safety, efficiency, and sustainability. The details of the pyrolysis mechanism are not readily available fro...
Pyrolysis Temperature Cellulose Deep Learning Molecular Dynamics Simulation
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Calculation of solvation force in molecular dynamics simulation by deep-learning method.

Biophysical journal Mar 4, 2024
Electrostatic calculations are generally used in studying the thermodynamics and kinetics of biomolecules in solvent. Generally, this is performed by solving the Poisson-Boltzmann equation on a large grid system, a process known to be time consuming....
Deep Learning Solvents Thermodynamics Molecular Dynamics Simulation Solubility
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Epitope Identification of an mGlu5 Receptor Nanobody Using Physics-Based Molecular Modeling and Deep Learning Techniques.

Journal of chemical information and modeling Feb 29, 2024
The world has witnessed a revolution in therapeutics with the development of biological medicines such as antibodies and antibody fragments, notably nanobodies. These nanobodies possess unique characteristics including high specificity and modulatory...
Epitopes Molecular Dynamics Simulation Rats Receptor, Metabotropic Glutamate 5 Protein Conformation Animals Models, Molecular Epitope Mapping Single-Domain Antibodies Deep Learning
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Computational discovery of novel FYN kinase inhibitors: a cheminformatics and machine learning-driven approach to targeted cancer and neurodegenerative therapy.

Molecular diversity Feb 28, 2024
In this study, we explored the potential of novel inhibitors for FYN kinase, a critical target in cancer and neurodegenerative disorders, by integrating advanced cheminformatics, machine learning, and molecular simulation techniques. Our approach inv...
Cheminformatics Proto-Oncogene Proteins c-fyn Piperidines Protein Kinase Inhibitors Humans Neurodegenerative Diseases Machine Learning Molecular Dynamics Simulation Drug Discovery Neoplasms Antineoplastic Agents Molecular Docking Simulation
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Machine Learning Deciphered Molecular Mechanistics with Accurate Kinetic and Thermodynamic Prediction.

Journal of chemical theory and computation Feb 23, 2024
Time-lagged independent component analysis (tICA) and the Markov state model (MSM) have been extensively employed for extracting conformational dynamics and kinetic community networks from unbiased trajectory ensembles. However, these techniques may ...
Humans Myeloid Cell Leukemia Sequence 1 Protein Machine Learning Kinetics Markov Chains Thermodynamics Molecular Dynamics Simulation
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Design of target specific peptide inhibitors using generative deep learning and molecular dynamics simulations.

Nature communications Feb 21, 2024
We introduce a computational approach for the design of target-specific peptides. Our method integrates a Gated Recurrent Unit-based Variational Autoencoder with Rosetta FlexPepDock for peptide sequence generation and binding affinity assessment. Sub...
Protein Binding NF-kappa B Molecular Dynamics Simulation beta Catenin Peptides Deep Learning
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Neural network informed photon filtering reduces fluorescence correlation spectroscopy artifacts.

Biophysical journal Feb 20, 2024
Fluorescence correlation spectroscopy (FCS) techniques are well-established tools to investigate molecular dynamics in confocal and super-resolution microscopy. In practice, users often need to handle a variety of sample- or hardware-related artifact...
Artifacts Photons Neural Networks, Computer Spectrometry, Fluorescence Molecular Dynamics Simulation
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Poisson-Boltzmann-based machine learning model for electrostatic analysis.

Biophysical journal Feb 15, 2024
Electrostatics is of paramount importance to chemistry, physics, biology, and medicine. The Poisson-Boltzmann (PB) theory is a primary model for electrostatic analysis. However, it is highly challenging to compute accurate PB electrostatic solvation ...
Molecular Dynamics Simulation Static Electricity Machine Learning Poisson Distribution Thermodynamics
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Identification of characteristic genes and herbal compounds for the treatment of psoriasis based on machine learning and molecular dynamics simulation.

Journal of biomolecular structure & dynamics Feb 12, 2024
Psoriasis brings economic and mental burdens to patients, the exact etiology and pathogenesis of psoriasis are still unclear. Compounds of herbal medicine have the potential for psoriasis treatment. This study aims to explore the characteristic genes...
Machine Learning Molecular Docking Simulation Humans Psoriasis Molecular Dynamics Simulation
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