scholarly journals A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule

2019 ◽  
Author(s):  
Daniel Cole ◽  
Letif Mones ◽  
Gábor Csányi
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Gaussian Approximation ◽  
P38 Map Kinase ◽  
Simulation Software ◽  
Free Energies ◽  
Analytical Potential ◽  
Leukotriene A4 Hydrolase ◽  
Leukotriene A4 ◽  
Binding Free Energies

<div><div><div><p>One limitation of the accuracy of computational predictions of protein–ligand binding free energies is the fixed functional form of the intramolecular component of the molecular mechanics force fields. Here, we employ the kernel regression machine learning technique to construct an analytical potential, using the Gaussian Approximation Potential software and framework, that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine. Challenges linked to the high dimensionality of the configurational space of the molecule are overcome by developing an iterative training protocol and employing a representation that separates short and long range interactions. The analytical model is connected to the MCPRO simulation software, which allows us to perform Monte Carlo simulations of the small molecule bound to two proteins, p38 MAP kinase and leukotriene A4 hydrolase, as well as in water. We demonstrate that the accuracy of our machine learning based intramolecular model is retained in the condensed phase, and that corrections to absolute protein–ligand binding free energies of up to 2 kcal/mol are obtained.</p></div></div></div>

scholarly journals A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule

2019 ◽  
Author(s):  
Daniel Cole ◽  
Letif Mones ◽  
Gábor Csányi
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Gaussian Approximation ◽  
P38 Map Kinase ◽  
Simulation Software ◽  
Free Energies ◽  
Analytical Potential ◽  
Leukotriene A4 Hydrolase ◽  
Leukotriene A4 ◽  
Binding Free Energies

<div><div><div><p>One limitation of the accuracy of computational predictions of protein-ligand binding free energies is the fixed functional form of the intramolecular component of the molecular mechanics force fields. Here, we employ the kernel regression machine learning technique to construct an analytical potential, using the Gaussian Approximation Potential software and framework, that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine. Challenges linked to the high dimensionality of the configurational space of the molecule are overcome by developing an iterative training protocol and employing a representation that separates short and long range interactions. The analytical model is connected to the MCPRO simulation software, which allows us to perform Monte Carlo simulations of the small molecule bound to two proteins, p38 MAP kinase and leukotriene A4 hydrolase, as well as in water. We demonstrate corrections to absolute protein-ligand binding free energies obtained with our machine learning based intramolecular model of up to 2 kcal/mol.</p></div></div></div>

scholarly journals A Machine Learning Based Intramolecular Potential for a Flexible Organic Molecule

2019 ◽  
Author(s):  
Daniel Cole ◽  
Letif Mones ◽  
Gábor Csányi
Keyword(s):  
Machine Learning ◽  
Ligand Binding ◽  
Gaussian Approximation ◽  
P38 Map Kinase ◽  
Simulation Software ◽  
Free Energies ◽  
Analytical Potential ◽  
Leukotriene A4 Hydrolase ◽  
Leukotriene A4 ◽  
Binding Free Energies

<div><div><div><p>One limitation of the accuracy of computational predictions of protein–ligand binding free energies is the fixed functional form of the intramolecular component of the molecular mechanics force fields. Here, we employ the kernel regression machine learning technique to construct an analytical potential, using the Gaussian Approximation Potential software and framework, that reproduces the quantum mechanical potential energy surface of a small, flexible, drug-like molecule, 3-(benzyloxy)pyridin-2-amine. Challenges linked to the high dimensionality of the configurational space of the molecule are overcome by developing an iterative training protocol and employing a representation that separates short and long range interactions. The analytical model is connected to the MCPRO simulation software, which allows us to perform Monte Carlo simulations of the small molecule bound to two proteins, p38 MAP kinase and leukotriene A4 hydrolase, as well as in water. We demonstrate that the accuracy of our machine learning based intramolecular model is retained in the condensed phase, and that corrections to absolute protein–ligand binding free energies of up to 2 kcal/mol are obtained.</p></div></div></div>

scholarly journals Protein-ligand free energies of binding from full-protein DFT calculations: convergence and choice of exchange-correlation functional

2021 ◽  
Author(s):  
Lennart Gundelach ◽  
Christofer S Tautermann ◽  
Thomas Fox ◽  
Chris-Kriton Skylaris
Keyword(s):  
Drug Discovery ◽  
Ligand Binding ◽  
Dft Calculations ◽  
Quantum Mechanical ◽  
Free Energies ◽  
Ligand Interaction ◽  
Exchange Correlation ◽  
Ligand Free ◽  
Protein Ligand Interaction ◽  
Binding Free Energies

The accurate prediction of protein-ligand binding free energies with tractable computational methods has the potential to revolutionize drug discovery. Modeling the protein-ligand interaction at a quantum mechanical level, instead of...

scholarly journals Alchemical Absolute Protein-Ligand Binding Free Energies for Drug Design

2021 ◽  
Author(s):  
Yuriy Khalak ◽  
Gary Tresdern ◽  
Matteo Aldeghi ◽  
Hannah Magdalena Baumann ◽  
David L. Mobley ◽  
...  
Keyword(s):  
Free Energy ◽  
Drug Discovery ◽  
Drug Design ◽  
Ligand Binding ◽  
Binding Free Energy ◽  
Free Energy Calculations ◽  
Free Energies ◽  
Energy Calculations ◽  
Binding Free Energy Calculations ◽  
Binding Free Energies

The recent advances in relative protein-ligand binding free energy calculations have shown the value of alchemical methods in drug discovery. Accurately assessing absolute binding free energies, although highly desired, remains...

Receptor–Ligand Binding Free Energies from a Consecutive Histograms Monte Carlo Sampling Method

2020 ◽  
Vol 16 (10) ◽  
pp. 6645-6655
Author(s):  
Hao Liu ◽  
Jianpeng Deng ◽  
Zhou Luo ◽  
Yawei Lin ◽  
Kenneth M. Merz ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Ligand Binding ◽  
Sampling Method ◽  
Monte Carlo Sampling ◽  
Free Energies ◽  
Receptor Ligand ◽  
Binding Free Energies

A water-swap reaction coordinate for the calculation of absolute protein–ligand binding free energies

2011 ◽  
Vol 134 (5) ◽  
pp. 054114 ◽  
Author(s):  
Christopher J. Woods ◽  
Maturos Malaisree ◽  
Supot Hannongbua ◽  
Adrian J. Mulholland
Keyword(s):  
Ligand Binding ◽  
Reaction Coordinate ◽  
Free Energies ◽  
Binding Free Energies

scholarly journals Binding thermodynamics of host-guest systems with SMIRNOFF99Frosst 1.0.5 from the Open Force Field Initiative

2019 ◽  
Author(s):  
David Slochower ◽  
Niel Henriksen ◽  
Lee-Ping Wang ◽  
John Chodera ◽  
David Mobley ◽  
...  
Keyword(s):  
Ligand Binding ◽  
Force Field ◽  
Small Molecule ◽  
Force Fields ◽  
Free Energy Calculations ◽  
Model Systems ◽  
Conformational Space ◽  
Free Energies ◽  
Binding Thermodynamics ◽  
Binding Free Energies

<div><div><div><p>Designing ligands that bind their target biomolecules with high affinity and specificity is a key step in small- molecule drug discovery, but accurately predicting protein-ligand binding free energies remains challenging. Key sources of errors in the calculations include inadequate sampling of conformational space, ambiguous protonation states, and errors in force fields. Noncovalent complexes between a host molecule with a binding cavity and a drug-like guest molecules have emerged as powerful model systems. As model systems, host-guest complexes reduce many of the errors in more complex protein-ligand binding systems, as their small size greatly facilitates conformational sampling, and one can choose systems that avoid ambiguities in protonation states. These features, combined with their ease of experimental characterization, make host-guest systems ideal model systems to test and ultimately optimize force fields in the context of binding thermodynamics calculations.</p><p><br></p><p>The Open Force Field Initiative aims to create a modern, open software infrastructure for automatically generating and assessing force fields using data sets. The first force field to arise out of this effort, named SMIRNOFF99Frosst, has approximately one tenth the number of parameters, in version 1.0.5, compared to typical general small molecule force fields, such as GAFF. Here, we evaluate the accuracy of this initial force field, using free energy calculations of 43 α and β-cyclodextrin host-guest pairs for which experimental thermodynamic data are available, and compare with matched calculations using two versions of GAFF. For all three force fields, we used TIP3P water and AM1-BCC charges. The calculations are performed using the attach-pull-release (APR) method as implemented in the open source package, pAPRika. For binding free energies, the root mean square error of the SMIRNOFF99Frosst calculations relative to experiment is 0.9 [0.7, 1.1] kcal/mol, while the corresponding results for GAFF 1.7 and GAFF 2.1 are 0.9 [0.7, 1.1] kcal/mol and 1.7 [1.5, 1.9] kcal/mol, respectively, with 95% confidence ranges in brackets. These results suggest that SMIRNOFF99Frosst performs competitively with existing small molecule force fields and is a parsimonious starting point for optimization.</p></div></div></div>

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