Neural Network Force Fields for Metal Growth Based on Energy Decompositions

2020 ◽  
Vol 11 (4) ◽  
pp. 1364-1369 ◽  
Author(s):  
Qin Hu ◽  
Mouyi Weng ◽  
Xin Chen ◽  
Shucheng Li ◽  
Feng Pan ◽  
...  
Keyword(s):  
Neural Network ◽  
Force Fields

Neural network force fields for simple metals and semiconductors: construction and application to the calculation of phonons and melting temperatures

2019 ◽  
Vol 21 (12) ◽  
pp. 6506-6516 ◽  
Author(s):  
Mário R. G. Marques ◽  
Jakob Wolff ◽  
Conrad Steigemann ◽  
Miguel A. L. Marques
Keyword(s):  
Neural Network ◽  
Force Fields ◽  
Practical Procedure ◽  
Melting Temperatures

We present a practical procedure to obtain reliable and unbiased neural network based force fields for solids.

Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields

2020 ◽  
Author(s):  
Aayush Gupta
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Force Fields ◽  
Md Simulations ◽  
Interaction Sites ◽  
Main Protease ◽  
Rational Drug ◽  
Interaction Map ◽  
Novel Coronavirus ◽  
Approved Drugs

<div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p>With the current pandemic situation caused by a novel coronavirus disease (COVID-19), there is an urgent call to develop a working therapeutic against it. Efficient computations aid to minimize the efforts by identifying a subset of drugs that can potentially bind to COVID-19 main protease or target protein (M<sup>PRO</sup>). The results of computations are always accompanied by their accuracy which depends on the details described by the model used. Machine learning models trained on millions of points and with unmatched accuracies are the best bet to employ in the process. In this work, I first identified and described the interaction sites of M<sup>PRO</sup> protein using a geometric deep learning model. Secondly, I conducted virtual screening (at one of the sites identified) on FDA approved drugs and picked 91 drugs having the highest binding affinity (below -8.0 kcal/mol). Then, I carried out 10 ns of molecular dynamics (MD) simulations using classical force fields and classified 37 drugs to be binding (includes drugs like Lopinavir, Saquinavir, Indinavir etc.) based on RMSD between MD-binding trajectories. To drastically improve the dynamics profile of selected 37 drugs, I brought in the highly accurate neural network force field (ANI) trained on coupled-cluster methods (CCSD(T)) data points and performed 1 ns of binding dynamics of each drug with protein. With the accurate approach, 19 drugs were qualified based on their RMSD cutoffs, and again with their free energy (ANI/MM/PBSA) computations another 7 drugs were rejected. The final selection of 12 drugs was validated based on MD trajectory clustering approach where 11 of 12 drugs (Targretin, Eltrombopag, Rifaximin, Deflazacort, Ergotamine, Doxazosin, Lastacaft, Rifampicin, Victrelis, Trajenta, Toposar, Indinavir) were confirmed to be binding. Further investigations were made to study their interactions with the protein and an accurate 2D- interaction map was generated. These findings and mapping of drug-protein interactions are highly accurate and could be potentially used to guide rational drug discovery against the COVID-19. </p> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>

scholarly journals Neural network reactive force field for C, H, N, and O systems

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Pilsun Yoo ◽  
Michael Sakano ◽  
Saaketh Desai ◽  
Md Mahbubul Islam ◽  
Peilin Liao ◽  
...  
Keyword(s):  
Neural Network ◽  
Force Field ◽  
Density Functional ◽  
Force Fields ◽  
High Energy ◽  
Reactive Force ◽  
Dispersion Interactions ◽  
Reactive Force Field ◽  
Wide Range ◽  
Reactive Force Fields

AbstractReactive force fields have enabled an atomic level description of a wide range of phenomena, from chemistry at extreme conditions to the operation of electrochemical devices and catalysis. While significant insight and semi-quantitative understanding have been drawn from such work, the accuracy of reactive force fields limits quantitative predictions. We developed a neural network reactive force field (NNRF) for CHNO systems to describe the decomposition and reaction of the high-energy nitramine 1,3,5-trinitroperhydro-1,3,5-triazine (RDX). NNRF was trained using energies and forces of a total of 3100 molecules (11,941 geometries) and 15 condensed matter systems (32,973 geometries) obtained from density functional theory calculations with semi-empirical corrections to dispersion interactions. The training set is generated via a semi-automated iterative procedure that enables refinement of the NNRF until a desired accuracy is attained. The root mean square (RMS) error of NNRF on a testing set of configurations describing the reaction of RDX is one order of magnitude lower than current state of the art potentials.

scholarly journals Profiling Molecular Simulations of SARS-CoV-2 Main Protease (Mpro) Binding to Repurposed Drugs Using Neural Network Force Fields

2020 ◽  
Author(s):  
Aayush Gupta
Keyword(s):  
Neural Network ◽  
Protein Interactions ◽  
Force Fields ◽  
Network Models ◽  
Md Simulations ◽  
Cluster Method ◽  
Neural Network Models ◽  
Interaction Sites ◽  
Main Protease ◽  
Novel Coronavirus

<p> </p><div> <div> <div> <p> </p><div> <div> <div> <p> </p><div> <div> <div> <p>With the current pandemic situation caused by a novel coronavirus disease (COVID-19), there is an urgent call to develop a working therapeutic against it. Efficient computations can minimize the efforts by identifying a subset of drugs that can potentially bind to the COVID-19 main protease or target protein (M<sup>PRO</sup>). The results of computations are accompanied by an evaluation of their accuracy, which depends on the details described by the model used. Neural network models trained on millions of points and with unmatched accuracies are the best approach to employ in this process. In this work, I first identified and described the interaction sites of the M<sup>PRO</sup> protein using a geometric deep learning model. Second, I conducted virtual screening (at one of the sites identified) on FDA-approved drugs and selected 91 drugs with the highest binding affinities (below -8.0 kcal/mol). Then, I conducted 10 ns of molecular dynamics (MD) simulations using classical force fields and classified 37 drugs to be binding (including Lopinavir, Saquinavir, and Indinavir) based on the RMSD between MD-binding trajectories. To drastically improve the dynamics profile of the 37 selected drugs, I used the highly accurate neural network force field (ANI) method trained on coupled-cluster method (CCSD(T)/CBS) data points and performed 1 ns of binding dynamics for each drug with the protein. Using this approach, 19 drugs were qualified based on their RMSD cutoffs, and based on free energy (ANI/MM/PBSA) computations, 7 of the drugs were rejected. The final selection of 12 drugs was validated based on an MD trajectory clustering approach where 11 of the 12 drugs (Targretin, Eltrombopag, Rifaximin, Deflazacort, Ergotamine, Doxazosin, Lastacaft, Rifampicin, Victrelis, Trajenta, Toposar, and Indinavir) were confirmed to exhibit binding. Further investigations were performed to study their interactions with the protein and an accurate 2D-interaction map was generated. These findings and mappings of drug-protein interactions are highly accurate and may be potentially used to guide rational drug discovery against COVID-19.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div>

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