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Fragment Pose Prediction Using Non-Equilibrium Candidate Monte Carlo and Molecular Dynamics Simulations

revised on 04.11.2019 and posted on 12.11.2019 by Nathan M. Lim, Meghan Osato, Gregory L. Warren, David L. Mobley
Part of early stage drug discovery involves determining how molecules may bind to the target protein. Through understanding where and how molecules bind, chemists can begin to build ideas on how to design improvements to increase binding affinities. In this retrospective study, we compare how computational approaches like docking, molecular dynamics (MD) simulations, and a non-equilibrium candidate Monte Carlo (NCMC) based method (NCMC+MD) perform in predicting binding modes for a set of 12 fragment-like molecules which bind to soluble epoxide hydrolase. We evaluate each method's effectiveness in identifying the dominant binding mode and finding any additional binding modes (if any). Then, we compare our predicted binding modes to experimentally obtained X-ray crystal structures.
We dock each of the 12 small molecules into the apo-protein crystal structure and then run simulations up to 1 microsecond each. Small and fragment-like molecules likely have smaller energy barriers separating different binding modes by virtue of relatively fewer and weaker interactions relative to drug-like molecules, and thus likely undergo more rapid binding mode transitions. We expect, thus, to see more rapid transitions betweeen binding modes in our study.

Following this, we build Markov State Models (MSM) to define our stable ligand binding modes. We investigate if adequate sampling of ligand binding modes and transitions between them can occur at the microsecond timescale using traditional MD or a hybrid NCMC+MD simulation approach. Our findings suggest that even with small fragment-like molecules, we fail to sample all the crystallographic binding modes using microsecond MD simulations, but using NCMC+MD we have better success in sampling the crystal structure while obtaining the correct populations.


NIH 1R01GM108889-01

NIH 1R01GM124270-01A1


Email Address of Submitting Author


University of California, Irvine


United States

ORCID For Submitting Author


Declaration of Conflict of Interest

DLM is a member of the Scientific Advisory Board of OpenEye Scientific Software and an Open Science Fellow with Silicon Therapeutics.

Version Notes

Updates/improves clarity of main text, changes what is in supporting information vs main text.



Read the published paper

in Journal of Chemical Theory and Computation

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