Molecular Dynamics Simulation

Machine learning in biological physics: From biomolecular prediction to design.

Proceedings of the National Academy of Sciences of the United States of America Jun 24, 2024

Machine learning has been proposed as an alternative to theoretical modeling when dealing with complex problems in biological physics. However, in this perspective, we argue that a more successful approach is a proper combination of these two methodo...

Neural Networks, Computer Machine Learning Proteins Molecular Dynamics Simulation Protein Engineering

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Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

Journal of chemical theory and computation Jun 18, 2024

Accurately describing long-range interactions is a significant challenge in molecular dynamics (MD) simulations of proteins. High-quality long-range potential is also an important component of the range-separated machine learning force field. This st...

Dipeptides Protein Conformation Static Electricity Proteins Molecular Dynamics Simulation Thermodynamics Neural Networks, Computer

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A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors.

International journal of molecular sciences Jun 14, 2024

G-protein coupled receptors (GPCRs) are transmembrane proteins that transmit signals from the extracellular environment to the inside of the cells. Their ability to adopt various conformational states, which influence their function, makes them cruci...

Receptors, G-Protein-Coupled Molecular Dynamics Simulation Protein Conformation Receptors, Adrenergic, beta-2 Humans Deep Learning

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Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics.

Molecules (Basel, Switzerland) Jun 13, 2024

Intracellular tau fibrils are sources of neurotoxicity and oxidative stress in Alzheimer's. Current drug discovery efforts have focused on molecules with tau fibril disaggregation and antioxidation functions. However, recent studies suggest that memb...

Molecular Dynamics Simulation Amyloid Catechin Humans Molecular Docking Simulation Machine Learning tau Proteins Protein Binding Protein Aggregates Antioxidants

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A Machine Learning Approach to Identify Key Residues Involved in Protein-Protein Interactions Exemplified with SARS-CoV-2 Variants.

International journal of molecular sciences Jun 13, 2024

Human infection with the coronavirus disease 2019 (COVID-19) is mediated by the binding of the spike protein of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) to the human angiotensin-converting enzyme 2 (ACE2). The frequent mutatio...

Protein Interaction Domains and Motifs Molecular Dynamics Simulation Angiotensin-Converting Enzyme 2 Binding Sites Spike Glycoprotein, Coronavirus COVID-19 Mutation Humans SARS-CoV-2 Machine Learning Protein Binding

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Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.

Journal of chemical information and modeling Jun 6, 2024

Determining the viability of a new drug molecule is a time- and resource-intensive task that makes computer-aided assessments a vital approach to rapid drug discovery. Here we develop a machine learning algorithm, iMiner, that generates novel inhibit...

Algorithms Proteins Artificial Intelligence Ligands Molecular Dynamics Simulation Molecular Docking Simulation Machine Learning Data Mining Drug Design

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Screening structure and predicting toxicity of pesticide adjuvants using molecular dynamics simulation and machine learning for minimizing environmental impacts.

The Science of the total environment Jun 6, 2024

Surfactants as synergistic agents are necessary to improve the stability and utilization of pesticides, while their use is often accompanied by unexpected release into the environment. However, there are no efficient strategies available for screenin...

Water Pollutants, Chemical Surface-Active Agents Molecular Dynamics Simulation Machine Learning Animals Octoxynol Zebrafish Pesticides

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Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine-Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials.

Molecules (Basel, Switzerland) Jun 6, 2024

Rare tautomeric forms of nucleobases can lead to Watson-Crick-like (WC-like) mispairs in DNA, but the process of proton transfer is fast and difficult to detect experimentally. NMR studies show evidence for the existence of short-time WC-like guanine...

Base Pairing Molecular Dynamics Simulation Thymine Thermodynamics Machine Learning Quantum Theory Protons Guanine DNA

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Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.

Physical chemistry chemical physics : PCCP Jun 6, 2024

Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conventional pharmacophore screening approach has limitations in conformation sampling and data mining. He...

Machine Learning Antineoplastic Agents Cell Line, Tumor Humans Drug Repositioning Fluvastatin Molecular Dynamics Simulation Myeloid Cell Leukemia Sequence 1 Protein Quantum Theory Pharmacophore

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Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

Molecules (Basel, Switzerland) Jun 5, 2024

CDK6 plays a key role in the regulation of the cell cycle and is considered a crucial target for cancer therapy. In this work, conformational transitions of CDK6 were identified by using Gaussian accelerated molecular dynamics (GaMD), deep learning (...

Binding Sites Thermodynamics Normal Distribution Cyclin-Dependent Kinase 6 Molecular Dynamics Simulation Protein Conformation Protein Kinase Inhibitors Protein Binding Principal Component Analysis Humans Deep Learning

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Machine learning in biological physics: From biomolecular prediction to design.

Developing a Differentiable Long-Range Force Field for Proteins with E(3) Neural Network-Predicted Asymptotic Parameters.

A Study on the Robustness and Stability of Explainable Deep Learning in an Imbalanced Setting: The Exploration of the Conformational Space of G Protein-Coupled Receptors.

Exploring Tau Fibril-Disaggregating and Antioxidating Molecules Binding to Membrane-Bound Amyloid Oligomers Using Machine Learning-Enhanced Docking and Molecular Dynamics.

A Machine Learning Approach to Identify Key Residues Involved in Protein-Protein Interactions Exemplified with SARS-CoV-2 Variants.

Mining for Potent Inhibitors through Artificial Intelligence and Physics: A Unified Methodology for Ligand Based and Structure Based Drug Design.

Screening structure and predicting toxicity of pesticide adjuvants using molecular dynamics simulation and machine learning for minimizing environmental impacts.

Electronic and Nuclear Quantum Effects on Proton Transfer Reactions of Guanine-Thymine (G-T) Mispairs Using Combined Quantum Mechanical/Molecular Mechanical and Machine Learning Potentials.

Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.

Unveiling Conformational States of CDK6 Caused by Binding of Vcyclin Protein and Inhibitor by Combining Gaussian Accelerated Molecular Dynamics and Deep Learning.

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