Machine learning estimates of natural product conformational energies.
Machine learning has been used for estimation of potential energy surfaces to speed up molecular dynamics simulations of small systems. We demonstrate that this approach is feasible for significantly larger, structurally complex molecules, taking the natural product Archazolid A, a potent inhibitor of vacuolar-type ATPase, from the myxobacterium Archangium gephyra as an example. Our model estimates energies of new conformations by exploiting information from previous calculations via Gaussian process regression. Predictive variance is used to assess whether a conformation is in the interpolation region, allowing a controlled trade-off between prediction accuracy and computational speed-up. For energies of relaxed conformations at the density functional level of theory (implicit solvent, DFT/BLYP-disp3/def2-TZVP), mean absolute errors of less than 1 kcal/mol were achieved. The study demonstrates that predictive machine learning models can be developed for structurally complex, pharmaceutically relevant compounds, potentially enabling considerable speed-ups in simulations of larger molecular structures.
Department of Chemistry and Applied Biosciences, Eidgenössische Technische Hochschule (ETH), Zürich, Switzerland.
Matthias
M
Rupp
Department of Pharmaceutical Chemistry, Eberhard Karls University, Tübingen, Germany.
Matthias R
MR
Bauer
Department of Pharmaceutical Chemistry, Eberhard Karls University, Tübingen, Germany.
Rainer
R
Wilcken
Department of Pharmaceutical Chemistry, Eberhard Karls University, Tübingen, Germany.
Andreas
A
Lange
Department of Chemistry and Applied Biosciences, Eidgenössische Technische Hochschule (ETH), Zürich, Switzerland.
Michael
M
Reutlinger
Department of Pharmaceutical Chemistry, Eberhard Karls University, Tübingen, Germany.
Frank M
FM
Boeckler
Department of Chemistry and Applied Biosciences, Eidgenössische Technische Hochschule (ETH), Zürich, Switzerland.
Gisbert
G
Schneider
D000251:Adenosine Triphosphatases / Q000737:chemistry; D000465:Algorithms; D001185:Artificial Intelligence; D002626:Chemistry, Pharmaceutical; D019295:Computational Biology / Q000379:methods; D004791:Enzyme Inhibitors / Q000737:chemistry*; D018942:Macrolides / Q000737:chemistry*; D009682:Magnetic Resonance Spectroscopy; D008956:Models, Chemical; D056004:Molecular Dynamics Simulation; D015394:Molecular Structure; D009231:Myxococcales / Q000378:metabolism; D016011:Normal Distribution; D025341:Principal Component Analysis; D011487:Protein Conformation; D012984:Software; D013269:Stochastic Processes; D013844:Thiazoles / Q000737:chemistry*
D004791:Enzyme Inhibitors; D018942:Macrolides; D013844:Thiazoles; C519727:archazolid A; D000251:Adenosine Triphosphatases