Fast Nosé–Hoover thermostat: molecular dynamics in quasi-thermodynamic equilibrium

Sidler, Dominik; Riniker, Sereina

doi:10.1039/c8cp06800c

Cited by 14 publications

(6 citation statements)

References 70 publications

(113 reference statements)

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“…The LINCS algorithm 44 was implemented to maintain bond lengths involving hydrogen atoms. Temperature and pressure were regulated using the Nose−Hoover thermostat 45 and Parrinello−Rahman barostat, 46 , respectively. Simulations were performed with a 2 fs time step, and the system's coordinates were recorded every 10 ps.…”

Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Nnadili,

Okafor,

Olayiwola

et al. 2024

Ind. Eng. Chem. Res.

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Molecular design is a critical aspect of various scientific and industrial fields, where the properties of molecules hold significant importance. In this study, a 3-fold methodology design is presented that leverages the power of generative artificial intelligence (AI), predictive modeling, and reinforcement learning to create tailored molecules with desired properties. This model synergistically combines deep learning techniques with Self-Referencing Embedded Strings (SELFIES) molecular representation to build a generative model that generates valid molecules and a graphical neural network model that accurately forecasts molecular properties. The Variational Autoencoder (VAE) coupled with reinforcement learning helps refine molecule generation based on targeted attributes. Data from an experimental study involving surfactants were used to test the framework. A validation of the structural integrity of the molecules generated was conducted, and Tanimoto similarities were used to quantify the similarity and diversity between the original and generated molecular structures. Also, saliency maps for the generated surfactants were produced to identify the features explaining the property values. Lastly, molecular dynamics simulations were used to validate the stability of the generated molecules. The results showed that the proposed framework can effectively produce valid molecules within the set property threshold value.

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Section: Reinforcement Learning (Rl)mentioning

confidence: 99%

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Nnadili,

Okafor,

Olayiwola

et al. 2024

Ind. Eng. Chem. Res.

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show abstract

“…73 This was followed by a 1 ns equilibration step using Nose-Hoover 74 temperature coupling with the temperature kept at 303.15 K and pressure at 1 atm. Later each system was subjected to isobaricisothermal (NPT) ensemble MD production stimulation for 1 μs length under periodic boundary conditions having isotropic Parrinello-Rahman pressure coupling, 75 with pressure kept at 1 atm. The leapfrog algorithm was used for integrating the Newtonian equations of motions.…”

Section: Simulation Protocolmentioning

confidence: 99%

N‐glycosylation induced changes in tau protein dynamics reveal its role in tau misfolding and aggregation: A microsecond long molecular dynamics study

et al. 2022

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Various posttranslational modifications like hyperphosphorylation, O-GlcNAcylation, and acetylation have been attributed to induce the abnormal folding in tau protein.Recent in vitro studies revealed the possible involvement of N-glycosylation of tau protein in the abnormal folding and tau aggregation. Hence, in this study, we performed a microsecond long all atom molecular dynamics simulation to gain insights into the effects of N-glycosylation on Asn-359 residue which forms part of the microtubule binding region. Trajectory analysis of the stimulations coupled with essential dynamics and free energy landscape analysis suggested that tau, in its Nglycosylated form tends to exist in a largely folded conformation having high beta sheet propensity as compared to unmodified tau which exists in a large extended form with very less beta sheet propensity. Residue interaction network analysis of the lowest energy conformations further revealed that Phe378 and Lys353 are the functionally important residues in the peptide which helped in initiating the folding process and Phe378, Lys347, and Lys370 helped to maintain the stability of the protein in the folded state.

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“…After energy minimization, the relaxed system was submitted to a simulation time of 100ns with a time step of 2fs, using a Nose-Hoover thermostat at 300K and using Martyna-Tobias-Klein Barostats at 1.01325 bar to simulate the NPT ensemble [46]. In the simulation process, the system energy was calculated every 1.2ps and the trajectory was recorded every 4.8ps.…”

Section: Molecular Dynamics Simulationmentioning

confidence: 99%

Computational investigation of adenosine 5′-(α,β-methylene)-diphosphate (AMPCP) derivatives as ecto-5′-nucleotidase (CD73) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations

et al. 2022

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CD73, as a surface enzyme anchored on the outside of the cell membrane via glycosylphosphatidylinositol (GPI), can convert the AMP in the tumor cell microenvironment into adenosine to promote the growth of tumor cells. It has been overexpressed in many different types of human tumors, such as gastric cancer, pancreatic cancer, liver cancer and other tumor cells. Therefore, targeted inhibitors of CD73 are considered as potential tumor treatment methods. Due to the low bioavailability of nucleoside CD73 inhibitors, it is necessary to develop new inhibitors. In this study, through molecular docking, three-dimensional quantitative structure-activity relationship (3D-QSAR) and molecular dynamics (MD) simulations, a series of CD73 inhibitors were calculated and studied to reveal their structure-activity relationships. Through molecular docking studies, explore the possible mode of interaction between inhibitors and protein. Subsequently, a 3D-QSAR model was established by comparative molecular eld analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA). For the best CoMFA model, the Q 2 and R 2 values are 0.708 and 0.983, respectively, while for the best CoMSIA model, the Q 2 and R 2 values are 0.809 and 0.992, respectively. In addition, the stability of the complex formed by the two inhibitors and CD73 was evaluated by molecular dynamics simulation, and the results are consistent with the results of molecular docking and 3D-QSAR research. Finally, the binding free energy was calculated by the surface area method (MM-GBSA), and the results are consistent with the activities that Van Der Waals and Coulomb contribute the most during the binding process of the molecule to the CD73 protein. In conclusion, our research provides valuable information for the further development of CD73 inhibitors.

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Fast Nosé–Hoover thermostat: molecular dynamics in quasi-thermodynamic equilibrium

Abstract: An extension of the Nosé–Hoover thermostat equation for molecular dynamics (MD) simulation is introduced, which perturbs fast degrees of freedom out of canonical equilibrium, while preserving the average temperature of the system.

Cited by 14 publications

References 70 publications

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

Surfactant-Specific AI-Driven Molecular Design: Integrating Generative Models, Predictive Modeling, and Reinforcement Learning for Tailored Surfactant Synthesis

N‐glycosylation induced changes in tau protein dynamics reveal its role in tau misfolding and aggregation: A microsecond long molecular dynamics study

Computational investigation of adenosine 5′-(α,β-methylene)-diphosphate (AMPCP) derivatives as ecto-5′-nucleotidase (CD73) inhibitors by using 3D-QSAR, molecular docking, and molecular dynamics simulations

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