Recurrent Neural Network Model to Retrieve the Long Range Spherical Potential Energy Function from Second Virial Coefficient

2002 ◽  
Vol 10 (2) ◽  
pp. 153-162 ◽  
Author(s):  
J.L. Neves ◽  
J.P. Braga ◽  
A.P. Braga ◽  
M.B. de Almeida
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Long Range ◽  
Recurrent Neural Network ◽  
Neural Network Model ◽  
Energy Function ◽  
Virial Coefficient ◽  
Second Virial Coefficient ◽  
Potential Energy Function ◽  
Spherical Potential

scholarly journals Schrodinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space

2019 ◽  
Author(s):  
James Stevenson ◽  
Leif D. Jacobson ◽  
Yutong Zhao ◽  
Chuanjie Wu ◽  
Jon Maple ◽  
...  
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Energy Function ◽  
Chemical Space ◽  
Binding Free Energy ◽  
Potential Energy Function ◽  
Free Energy Calculations ◽  
Previous State ◽  
Speed And Accuracy ◽  
Better Than

We have developed a neural network potential energy function for use in drug discovery, with chemical element support extended from 41% to 94% of druglike molecules based on ChEMBL. We expand on the work of Smith et al., with their highly accurate network for the elements H, C, N, O, creating a network for H, C, N, O, S, F, Cl, P. We focus particularly on the calculation of relative conformer energies, for which we show that our new potential energy function has an RMSE of 0.70 kcal/mol for prospective druglike molecule conformers, substantially better than the previous state of the art. The speed and accuracy of this model could greatly accelerate the parameterization of protein-ligand binding free energy calculations for novel druglike molecules.

scholarly journals Schrodinger-ANI: An Eight-Element Neural Network Interaction Potential with Greatly Expanded Coverage of Druglike Chemical Space

2019 ◽  
Author(s):  
James Stevenson ◽  
Leif D. Jacobson ◽  
Yutong Zhao ◽  
Chuanjie Wu ◽  
Jon Maple ◽  
...  
Keyword(s):  
Neural Network ◽  
Potential Energy ◽  
Energy Function ◽  
Chemical Space ◽  
Binding Free Energy ◽  
Potential Energy Function ◽  
Free Energy Calculations ◽  
Previous State ◽  
Speed And Accuracy ◽  
Better Than

We have developed a neural network potential energy function for use in drug discovery, with chemical element support extended from 41% to 94% of druglike molecules based on ChEMBL. We expand on the work of Smith et al., with their highly accurate network for the elements H, C, N, O, creating a network for H, C, N, O, S, F, Cl, P. We focus particularly on the calculation of relative conformer energies, for which we show that our new potential energy function has an RMSE of 0.70 kcal/mol for prospective druglike molecule conformers, substantially better than the previous state of the art. The speed and accuracy of this model could greatly accelerate the parameterization of protein-ligand binding free energy calculations for novel druglike molecules.

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