High-performance free energy calculations for drug discovery
The Advantages of Free Energy Perturbation Calculations
Achieving highly potent binding, while maintaining a host of other ligand properties required for safety and biological efficacy, is a primary objective of small molecule drug discovery. Historically, it has been challenging for free energy calculations to achieve the accuracy, reliability, ease of use, and throughput that are required to impact lead optimization in an industrial setting.
Thanks to recent advances in force fields and sampling algorithms, coupled with the availability of low-cost parallel computing, free energy calculations can now yield meaningful comparisons with experimental binding affinities. The confluence of these advances is allowing in silico simulations to contribute to real-life drug discovery efforts by providing better synthesis decisions during lead optimization.
State-of-the-art Force Field:
The OPLS3 force field incorporates refined van der Waals parameters and partial charges, as well as >10,000 additional torsional parameters optimized for drug-like molecules. In addition, the OPLS3 force field contains improved parameters for proteins and nucleic acids, expanded small molecule torsional coverage, and off-atom charge sites to represent halogen bonding and aryl nitrogen lone pair interactions. These improvements have enabled the OPLS3 force field to achieve high accuracy in the modeling of protein dynamics, small molecule solvation and small conformational energetics, and protein-ligand binding.
The FEP+ GUI allows users to easily set up the desired perturbations without requiring expert knowledge of the complex calculations that are automated behind the scenes. Powerful analysis tools also make it possible to visualize and examine the computed results.
Incorporation of the REST (replica exchange with solute tempering) enhanced sampling methodology enables detailed-balance-preserving simulation of a selected subsystem at a higher effective temperature, thereby focusing sampling efforts to most efficiently traverse the relevant phase space.
The FEP Mapper interface elucidates the network of transformations and facilitates the analysis of the consistency and convergence of the simulation results by identifying sub-calculations that may require attention as well as providing error estimates from individual simulations.
Optimization of the FEP+ algorithm to take full advantage of the Desmond GPU MD engine enabling 2 to 4 ligands to be scored per day on a relatively inexpensive 4 GPU server.
Citations and Acknowledgements
Schrödinger Release 2017-1: Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2017. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2017.
ö Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W., "Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field," J. Chem. Theory Comput., 2010, 6, 1509–1519
ö Guo, Z.; Mohanty, U.; Noehre, J.; Sawyer, T. K.; Sherman, W.; Krilov, G., "Probing the α-Helical Structural Stability of Stapled p53 Peptides: Molecular Dynamics Simulations and Analysis," Chem. Biol. Drug Des., 2010, 75, 348-359
Kevin J. Bowers, Edmond Chow, Huafeng Xu, Ron O. Dror, Michael P. Eastwood, Brent A. Gregersen, John L. Klepeis, Istvan Kolossvary, Mark A. Moraes, Federico D. Sacerdoti, John K. Salmon, Yibing Shan, and David E. Shaw, "Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters," Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, 2006, November 11-17
ö "Prospective Evaluation of Free Energy Calculations for the Prioritization of Cathepsin L Inhibitors"Kuhn, B.; Tichy, M.; Wang, L.; Robinson, S.; Martin, R.E.; Kuglstatter, A.; Benz, J.; Giroud, M.; Schirmeister, T.; Abel, R.; Diederich, F.; Hert, J., J. Med. Chem., 2017, Article ASAP, doi: 10.1021/acs.jmedchem.6b01881
ö "Accelerating drug discovery through tight integration of expert molecular design and predictive scoring"Abel, R.; Mondal, S.; Masse, C.; Greenwood, J.; Harriman, G.; Ashwell, M.A.; Bhat, S.; Wester, R.; Frye, L.; Kapeller, R.; Friesner, R.A., Curr. Opin. Struct. Biol., 2017, 43, 38-44
ö "Accurate Modeling of Scaffold Hopping Transformations in Drug Discovery"Wang, L.; Deng, Y.; Wu, Y.; Kim, B.; LeBard, D.N.; Wandschneider, D.; Beachy, M.; Friesner, R.A.; Abel, R., J. Chem. Theory Comput., 2017, 13 (1), 42–54
ö "Predicting Binding Affinities for GPCR Ligands Using Free-Energy Perturbation"Lenselink, E.B.; Louvel, J.; Forti, A.F.; van Veldhoven, J.P.D.; de Vries, H.; Mulder-Krieger, T.; McRobb, F.M.; Negri, A.; Goose, J.; Abel, R.; van Vlijmen, H.W.T.; Wang, L.; Harder, E.; Sherman, W.; IJzerman, A.P.; Beuming, T., ACS Omega, 2016, 1, 293-304
ö "OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins"Harder, E.; Damm, W.; Maple, J.; Wu, C.; Reboul, M.; Xiang, J.Y.; Wang, L.; Lupyan, D.; Dahlgren, M.K.; Knight, J.L.; Kaus, J.W.; Cerutti, D.S.; Krilov, G.; Jorgensen, W.L.; Abel, R.; Friesner, R.A., J. Chem. Theory Comput., 2016, 2 (1), 281–296
Accelerating Molecular Dynamics Simulations with GPUs
Molecular Dynamics (MD) and Free Energy Perturbation (FEP) calculations occur on time scales that are computationally demanding to simulate. A key factor in determining whether a simulation will take days, hours, or minutes to run is the hardware being used. The advent of GPU computing, however, has opened the door to a new world of computationally intensive simulations that would not have been possible even a few years ago. Desmond's high-performance Molecular Dynamics code, together with continuously improving computer hardware technologies are helping scientists push the boundaries of discovery further than ever before.
Figure 1: Comparison of GPU and CPU performance (ns/day) vs number of atoms (thousands) in sample simulations using the average of NVT and NPT ensembles. In general, using GPUs improve performance by 1-2 orders of magnitude compared to CPUs.
Schrödinger MD Compatible Systems by Exxact Corporation
Schrödinger has teamed up with Exxact Corporation to design a series of GPU computing systems that meet or exceed the requirements for Desmond MD and FEP calculations. Exxact works closely with the NVIDIA Tesla GPU team and is both a leading supplier and a Tesla Preferred Partner with NVIDIA. It offers both Workstations and Servers specifically tailored to run MD and FEP simulations with Schrödinger software.
|MD/FEP+ Workstations||MD/FEP+ Servers|
Schrödinger MD Compatible Systems by Exxact Corporation
- Designed to meet the requirements for Molecular Dynamics GPU Computing or highly complex Free Energy Perturbation calculations
- Optimized to meet or exceed the published performance benchmarks
- Preinstalled Desmond GPU computing solutions designed in collaboration with Schrödinger
- Every system is optimized and validated for your computing environment
- Fully customizable to meet your budget
- Tesla Preferred Partner: As a leading Tesla Preferred Partner with NVIDIA, Exxact Corporation works closely with the NVIDIA Tesla GPU team to ensure seamless factory development and support. Exxact prides itself on providing value-added service standards unmatched by any competitors.
- Scalable to Your Changing Needs: Exxact also offers multiple form factors should your computing needs change down the road, minimizing growing pains due to unpredictability.
- In-House Engineering and Design: Exxact GPU systems are built by in-house engineers and individually customizable for peak performance tailored to solving your unique and complex computing challenges.
- 3 Year Warranty: Each GPU system is engineer built and meticulously tested for absolute reliability and performance. Exxact stands behind its products by offering a 3-year warranty on its Tesla GPU systems, including parts and labor.
- Onsite Support: Besides remote assistance, Exxact also offers on-site technical support including 8x5 next business day, 24x7 next business day, and 24x7x4hr.