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>

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>

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 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)/CBS) 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, and 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>

Method of complex copper-zinc ore typification using neural network models

2020 ◽  
Vol 5 ◽  
pp. 140-147 ◽  
Author(s):  
T.N. Aleksandrova ◽  
◽  
E.K. Ushakov ◽  
A.V. Orlova ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Copper Zinc ◽  
Complex Copper

Digital twin of equipment as a basis for the consumer in digital production

2020 ◽  
Keyword(s):  
Neural Network ◽  
Tool Wear ◽  
Chip Formation ◽  
Network Models ◽  
Machining Accuracy ◽  
Neural Network Models ◽  
Digital Twin ◽  
The Neural Network ◽  
Digital Production ◽  
Cyberphysical System

The neural network models series used in the development of an aggregated digital twin of equipment as a cyber-physical system are presented. The twins of machining accuracy, chip formation and tool wear are examined in detail. On their basis, systems for stabilization of the chip formation process during cutting and diagnose of the cutting too wear are developed. Keywords cyberphysical system; neural network model of equipment; big data, digital twin of the chip formation; digital twin of the tool wear; digital twin of nanostructured coating choice

scholarly journals Prediction of Stress in Power Transformer Winding Conductors Using Artificial Neural Networks: Hyperparameter Analysis

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4242
Author(s):  
Fausto Valencia ◽  
Hugo Arcos ◽  
Franklin Quilumba
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Finite Element ◽  
Power Transformer ◽  
Network Models ◽  
Activation Function ◽  
Neural Network Models ◽  
Transformer Winding ◽  
Artificial Neural ◽  
Femm Software

The purpose of this research is the evaluation of artificial neural network models in the prediction of stresses in a 400 MVA power transformer winding conductor caused by the circulation of fault currents. The models were compared considering the training, validation, and test data errors’ behavior. Different combinations of hyperparameters were analyzed based on the variation of architectures, optimizers, and activation functions. The data for the process was created from finite element simulations performed in the FEMM software. The design of the Artificial Neural Network was performed using the Keras framework. As a result, a model with one hidden layer was the best suited architecture for the problem at hand, with the optimizer Adam and the activation function ReLU. The final Artificial Neural Network model predictions were compared with the Finite Element Method results, showing good agreement but with a much shorter solution time.

scholarly journals The Effectiveness of Ensemble-Neural Network Techniques to Predict Peak Uplift Resistance of Buried Pipes in Reinforced Sand

2021 ◽  
Vol 11 (3) ◽  
pp. 908
Author(s):  
Jie Zeng ◽  
Panagiotis G. Asteris ◽  
Anna P. Mamou ◽  
Ahmed Salih Mohammed ◽  
Emmanuil A. Golias ◽  
...  
Keyword(s):  
Neural Network ◽  
Numerical Models ◽  
Correlation Coefficients ◽  
Network Models ◽  
Small Scale ◽  
Offshore Platforms ◽  
Neural Network Models ◽  
Buried Pipes ◽  
Ensemble Techniques ◽  
Uplift Resistance

Buried pipes are extensively used for oil transportation from offshore platforms. Under unfavorable loading combinations, the pipe’s uplift resistance may be exceeded, which may result in excessive deformations and significant disruptions. This paper presents findings from a series of small-scale tests performed on pipes buried in geogrid-reinforced sands, with the measured peak uplift resistance being used to calibrate advanced numerical models employing neural networks. Multilayer perceptron (MLP) and Radial Basis Function (RBF) primary structure types have been used to train two neural network models, which were then further developed using bagging and boosting ensemble techniques. Correlation coefficients in excess of 0.954 between the measured and predicted peak uplift resistance have been achieved. The results show that the design of pipelines can be significantly improved using the proposed novel, reliable and robust soft computing models.

Sign in / Sign up

Export Citation Format

Share Document