High-performance molecular dynamics simulations

The Advantages of Molecular Dynamics Simulations

Molecular and condensed-phase systems are dynamic in nature; therefore analyzing their motions at the molecular and atomistic level is essential to understanding key physicochemical phenomena. For decades, there has been keen interest in modeling the dynamic aspects of various systems in both life science and materials science. Such systems include protein-ligand complexes, small molecules in mixed solvents, organic solids, and synthetic macromolecular complexes. Molecular dynamics (MD) simulation stands alone as the fundamental computational tool for capturing dynamic events of scientific interest in all these applications.

Recently in life science applications, static structure-based approaches, such as docking and virtual screening, have made important strides in advancing drug discovery. MD, especially when coupled with these other computational tools, will open the door to addressing the many drug discovery problems for which the dynamic nature of proteins cannot be ignored, as in the mechanisms of highly mobile membrane proteins and in ligand-induced conformational changes of active sites.

Another crucial area that can benefit from MD simulation is in the analysis of existing systems, and development of new materials for a wide variety of applications. Predicting dynamic properties such as thermophysical and mechanical properties of materials systems reliably using MD simulation is an enabling capability required to establish a “Materials by Design” framework in electronics, energy, and aerospace industries.

Many physicochemical phenomena of scientific interest both in life science and materials science occur on time scales that are computationally demanding to simulate. A high-performance MD code, together with continuously advancing computer hardware technologies, can be used to perform simulations on time scales that illuminate these important dynamic processes. Desmond, a newly developed MD code created by D. E. Shaw Research, provides parallel scalability, simulation throughput, and scientific accuracy to achieve these goals.

Exceptional performance:
Desmond achieves exceptional scalability on commodity Linux clusters with both typical and high-end networks.

State-of-the-art GPU acceleration technology:
With the latest graphics processing unit (GPU) technology implemented in Desmond, MD simulation can run up to 200 times faster than on CPU, which can bring up the time scale of interest by orders of magnitude.

Figure 1: demonstrating a range of over 10x to 64x increase in performance (ns/day) compared to standard 4 and 8 CPU processors.

*Per Single GPU

Superior accuracy:
Desmond excels in numerical accuracy, which helps to ensure proper modeling of certain thermodynamic relationships that depend on detailed balance. Time-reversible simulations can also be performed. Numerical rigor helps to maintain low energy drift throughout each simulation.

Trusted energetics:
Desmond provides a robust framework for the calculation of energies and forces for various force field models and is compatible with those models commonly used in both biomolecular and condensed-matter research, including CHARMM, AMBER, and OPLS.

Realistic simulations:
Desmond performs explicit solvent simulations with periodic boundary conditions using cubic, orthorhombic, truncated octahedron, rhombic dodecahedron, and arbitrary triclinic simulation boxes, and can be used to model explicit membrane systems under various conditions.

Quantitative predictions:
Desmond computes both absolute and relative solvation free energies as well as relative free energies of binding.

Easy-to-use interface:
Desmond supports automated simulation setup, including highly complex Free Energy Perturbation (FEP) calculations, multistage MD simulations with built-in simulation protocols, prediction of equation of states (EOS) at multiple temperatures, and prediction of dynamic responses at non-equilibrium states. An intuitive interface provides intelligent default settings and allows for rapid setup of computational experiments. Powerful analysis tools make it possible to visualize and examine computed results within the same Maestro modeling environment.

Citations and Acknowledgements

Schrödinger Release 2021-4: Desmond Molecular Dynamics System, D. E. Shaw Research, New York, NY, 2021. Maestro-Desmond Interoperability Tools, Schrödinger, New York, NY, 2021.

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

"Potency- and Selectivity-Enhancing Mutations of Conotoxins for Nicotinic Acetylcholine Receptors Can Be Predicted Using Accurate Free-Energy Calculations"

Katz D.; DiMattia M.A.; Sindhikara D.; Li H.; Abraham N.; Leffler A., , 2021, ,

"Quenching-Resistant Solid-State Photoluminescence of Graphene Quantum Dots: Reduction of π−π Stacking by Surface Functionalization with POSS, PEG, and HDA"

Park, M.; Jeong, Y.; Kim, H.S.; Lee, W.; Nam, S. H.; Lee, S.; Yoon, H.; Kim, J.; Yoo, S.; Jeon, S., , 2021, ,

"Molecular Stacking Effect in Small-Molecular OLEDs Prepared with Solution Process"

Lee, J.Y.; Kim, J.; Kim, H.; Min Suh, M.C., ACS Appl. Mater. Interfaces, 2020, 12 (20), 23244–23251

"Clobetasol Propionate Is a Heme-Mediated Selective Inhibitor of Human Cytochrome P450 3A5"

Wright, W.C.; Chenge, J.; Wang, J.; Girvan, H.M.; Yang, L.; Chai, S.C.; Huber, A.D.; Wu, J.; Oladimeji, P.O.; Munro, A.W.; Chen, T., J. Med. Chem., 2020, 63 (3), 1415–1433

ö "Estimation of electron and hole mobility of 50 homogeneous fullerene amorphous structures (C60, C58B2, C58N2 and C58NB) using a percolation corrected Marcus theory model"

Goldberg, A; Kwak, S; Hall, M.D.;.Matsuzawa, N.N.; Sasago, M; Arai, H; Fujii, E, Org. Electron., 2019, 105571, In Press

ö "GemSpot: A Pipeline for Robust Modeling of Ligands into CryoEM Maps"

Robertson, M.J.; van Zundert, G.C.P.; Borrelli, K.; Skiniotis, S., bioRxiv, 2019, 750778, Preprint

"Adsorption properties of graphene towards the ephedrine – A frequently used molecule in sport"

Armaković, S.; Armaković, S.; Tomić, B.T.; Pillai, R.R.; Panicker, C.Y., Comput. Theor. Chem., 2018, 1124 (15), 39-50

"Cheminformatics Analysis of Dynamic WNK-InhibitorInteractions"

Kuenemann, M.A.; Fourches, D., Mol. Inf., 2018, Article ASAP, DOI: 10.1002/minf.201700138

"Complexes of Zn(II) and Cd(II) with 2-acetylpyridine -aminoguanidine – Syntheses, structures and DFT calculations"

Radanović, M.; Rodić, M.; Vojinović-Ješić, L.; Armaković, S.; Armaković, S.; Leovac, V. , Inorganica Chimica Acta., 2018, In Press, doi: 10.1016/j.ica.2017.12.038.

"Understanding reactivity of two newly synthetized imidazole derivatives by spectroscopic characterization and computational study"

Hossain, M.; Renjith, T.; Mary, Y.; Resmi, K.; Armaković, S.; Armaković, S.; Ashis, N.; Vijayakumar, G.; Alsenoy, C., J. Molec. Struct., 2018, In Press,

"Predicting Binding Free Energies of PDE2 Inhibitors. The Difficulties of Protein Conformation"

Pérez-Benito, L.; Keränen, H.; van Vlijmen, H.; Tresadern, G., Nature, Scientific Reports, 2018, 8 (4883), doi:10.1038/s41598-018-23039-5

"Photocatalytic degradation of 4-amino-6-chlorobenzene-1,3-disulfonamide stable hydrolysis product of hydrochlorothiazide: Detection of intermediates and their toxicity"

Armaković, S.; Armaković, S.; Četojević-Simin D.; Šibul, F.; Abramović, B., Environ. Pollut., 2018, 233, 916-924

ö "Calculating Water Thermodynamics in the Binding Site of Proteins – Applications of WaterMap to Drug Discovery"

Cappel, D.; Sherman, W.; Beuming, T., Curr Top Med Chem., 2017, 17 (23), 2586-2598

"Relative binding affinity prediction of farnesoid X receptor in the D3R Grand Challenge 2 using FEP+"

Schindler, C.; Rippmann, F.; Kuhn, D., J Comput Aided Mol Des, 2017, (32)1, 254-272

"Characterizing the Chemical Space of ERK2 Kinase Inhibitors Using Descriptors Computed from Molecular Dynamics Trajectories"

Ash, J.; Fourches, D., J. Chem. Inf. Model., 2017, 57 (6), 1286–1299

"Unraveling the Orientation of Phosphors Doped in Organic Semiconducting Layers"

Moon, C.; Kim, K.; Kim, J., Nature Communications, 2017, 8, 791

ö "Acylguanidine Beta Secretase 1 Inhibitors: A Combined Experimental and Free Energy Perturbation Study"

Keränen, H.; Pérez-Benit, L.; Ciordia, M.; Delgado, F.; Steinbrecher, T.B.; Oehlrich, D.; van Vlijmen, H.; Trabanco, A.A.; Tresadern, G., J. Chem. Theory Comput., 2017, 13, 1439-1453

ö "Predicting the Effect of Amino Acid Single-Point Mutations on Protein Stability—Large-Scale Validation of MD-Based Relative Free Energy Calculations"

Steinbrecher, T.; Zhu, C.; Wang, L.; Abel, A.; Negron, C.; Pealman, D.; Feyfant, E.; Duan, J.; Sherman, W., J. Mol. Biol. , 2017, 429 (7), 948-963

ö "Multifaceted Peptide Assisted One-Pot Synthesis of Gold Nanoparticles for Plectin-1 Targeted Gemcitabine Delivery in Pancreatic Cancer"

Krishnendu, P.; Al-suraih, F.; Gonzalez-Rodriguez, G.; Dutta, S.K.; Wang, E.; Kwak, H.S.; Caulfield, T.R.; Coffer, J.L.; Bhattacharya, S., Nanoscale, 2017, 9, 15622-15634

"Combined spectroscopic, DFT, TD-DFT and MD study of newly synthesized thiourea derivative"

Menon, V.; Sheena, Y.; Shyma, M.; Panicker, C.Y.; Bielenica, A.; Armaković, S.; Armaković, S.; Alsenoy, C., J. Molec. Struct., 2017, 1155, 184-195

ö "Investigating Protein–Peptide Interactions Using the Schrödinger Computational Suite"

Bhachoo, J.; Beuming, T., Methods Mol Biol., 2017, 1561, 235-254

"Synthesis, spectroscopic analyses (FT-IR and NMR), vibrational study, chemical reactivity and molecular docking study and anti-tubercular activity of condensed oxadiazole and pyrazine derivatives"

El-Azab, A.; Mary, Y.; Abdel-Aziz, A.; Miniyar, P.; Armaković, S.; Armaković, S., J. Molec. Struct., 2017, 1155, 184-195

"Synthesis, characterization and computational study of the newly synthesized sulfonamide molecule"

Potla, K.; Suneetha, V.; Armaković, S.; Armaković, S.; Suchetan, P.A.; Giri, L.; Sreenivasa, R., J. Molec. Struct., 2017, 1153, 212-229

"Exploring the enantiorecognition mechanism of Cinchona alkaloid-based zwitterionic chiral stationary phases and the basic trans-paroxetine enantiomers"

Sardella, R.; Macchiarulo, A.; Urbinati, F.; Ianni, F.; Carotti, A.; Kohout, M.; Lindner, W.; Péter, A.; Ilisz, I., J. Sep. Sci., 2017, 41(6), 1199-1207

ö "A New Mixed All-Atom/Coarse-Grained Model: Application to Melittin Aggregation in Aqueous Solution"

Shelley, M.Y.; Selvan, M.E.; Zhao, J.; Babin, V.; Liao, C.; Li, J.; Shelley, J.C, J. Chem. Theory Comput., 2017, 13 (8), 3881–3897

"Examining the Feasibility of Using Free Energy Perturbation (FEP+) in Predicting Protein Stability"

Ford, M.C.; Babaoglu, K., J. Chem. Inf. Model., 2017, 57(6), 1276–1285

ö "Estimation of Charge Carrier Mobility in Amorphous Organic Materials Using Percolation Corrected Random-Walk Model"

Evansa, D.R.; Kwak, H.S.; Giesen, D.J.; Goldberg, G.; Halls, M.D.; Oh-ee, M., Organic Electronics, 2016, 29, 50-56

"Kosmotropism of Newly Synthesized 1-butyl-3-methylimidazolium Taurate Ionic Liquid: Experimental and Computational Study"

Tot, A.; Armaković, S.; Armaković, S.J.; Gadžurić, S.; Vraneš, M. , The Journal of Chemical Thermodynamics , 2016, 94, 85

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

"FT-IR, FT-Raman and NMR Characterization of 2-isopropyl-5-methylcyclohexyl quinoline-2-carboxylate and Investigation of its Reactive and Optoelectronic Properties by Molecular Dynamics Simulations and DFT Calculations"

Menon, V.V.; Fazal, E.; Mary, Y.S.; Panicker, C.Y.; Armaković, S.; Armaković, S.J.; Nagarajan, S.; Alsenoy, C.V.;, Journal of Molecular Structure, 2016, 1127, 124

"Fluorophores based on a minimal thienylthiazole core: towards multifunctional materials with solid state red emissions, solvatochromism and AIE behaviour"

Radhakrishnan, R.; Sreejalekshmi, KG, RSC Adv., 2016, 39, 32705-32709

"Towards Understanding the Unbound State of Drug Compounds: Implications for the Intramolecular Reorganization Energy Upon Binding"

Foloppe, N.; Chen, I;, Bioorg. Med. Chem., 2016, (16), 30172-9

ö "Free Energy Perturbation Calculation of Relative Binding Free Energy between Broadly Neutralizing Antibodies and the gp120 Glycoprotein of HIV-1"

Clark, A.J.; Gindin, T.; Zhang, B.; Wang, L.; Abel, R.; Murret, C.S.; Xu, F.; Bao, A.; Lu, N.J.; Zhou, T.; Kwong, P.D.; Shapiro, L.; Honig, B.; Friesner, R.A. , J. Mol. Biol., 2016, 16, 30516-2

"Application of Free Energy Perturbation for the Design of BACE1 Inhibitors "

Ciordia, M.; Pérez-Benito, L.; Delgado, F.; Trabanco, A. A.; Tresadern, G., J. Chem. Inf. Model., 2016, 56(9), 1856–1871

"Influence of electron acceptors on the kinetics of metoprolol photocatalytic degradation in TiO2 suspension. A combined experimental and theoretical study"

Armaković, S.J.; Armaković, S.; Finčur, N.L.; Šibul, F.; Vione, D.; Šetrajčić, J.P.; Abramović, B., RSC Advances, 2016, 5, 54589

ö "Relative Binding Free Energy Calculations Applied to Protein Homology Models"

Cappel, D.; Hall, M.L.; Lenselink, E.B.; Beuming, T.; Qi, J.; Bradner, J.; Sherman, W., J. Chem. Inf. Model., 2016, 56 (12), 2388–2400

ö "Accurate Binding Free Energy Predictions in Fragment Optimization"

Steinbrecher, T.B.; Dahlgren, M.; Cappel, D.; Lin, T.; Wang, L.; Krilov, G.; Abel, R.; Friesner, R.; Sherman, W., J. Chem. Inf. Model., 2015, 55 (11), 2411–2420

ö "How To Deal with Multiple Binding Poses in Alchemical Relative Protein−Ligand Binding Free Energy Calculations"

Kaus, J. W.; Harder, E.; Lin, T.; Abel, R.; McCammon, A.; Wang, L., J. Chem. Theory Comput., 2015, 11(6), 2670–2679

ö "Accurate and Reliable Prediction of Relative Ligand Binding Potency in Prospective Drug Discovery by Way of a Modern Free-Energy Calculation Protocol and Force Field"

Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M.K.; Greenwood, J.; et al., J. Am. Chem. Soc., 2015, 137 (7), 2695–2703

ö "Atomic-scale Simulation for the Analysis, Optimization and Accelerated Development of Organic Optoelectronic Materials"

Halls, M.D.; Yoshidome, D.; Mustard, T.J.; Goldberg, A.; Kwak, H.S.; Gavartin, J.L., Journal of the Imaging Society of Japan, 2015, 54(6), 561

ö "Melittin Aggregation in Aqueous Solutions: Insight from Molecular Dynamics Simulation"

Liao, C.; Selvan, M.E.; Zhao, J.; Slimovitch, J.L.; Schneebeli, S.T.; Shelley, M.; Shelley, J.C.; and Li, J., J. Phys. Chem. B., 2015, 119 (33), 10390–10398

"Molecular Dynamics Simulation Study of Sodium Dodecyl Sulfate Micelle: Water Penetration and Sodium Dodecyl Sulfate Dissociation"

Chun, B.J.; Choi, J.I.; Jang, S.S., Colloids and Surfaces A: Physicochemical and Engineering Aspects, 2015, 474, 36

"Initial Decomposition Reactions of Bicyclo-HMX [BCHMX or cis-1,3,4,6-Tetranitrooctahydroimidazo -[4,5-d]imidazole] from Quantum Molecular Dynamics Simulations"

Ye, C.-C., An, Q., Goddard, W.A.; Cheng, T.; Zybin, S.; Ju, X.-H.;, Journal of Physical Chemistry C, 2015, 119(5), 2290

"Crystal Structure of Antagonist Bound Human Lysophosphatidic Acid Receptor 1"

Chrencik, J.E.; Roth, C.B.; Terakado, M.; Kurata, H.; Omi, R.; Kihara, Y.; Warshaviak, D.; Nakade, S.; Asmar-Rovira, G.; Mileni, M.; Mizuno, H.; Griffith, M.T.; Rodgers, C.; Han, G.W.; Velasquez, J.; Chun, J.; Stevens, R.C.; Hanson, M.A., Cell, 2015, 161(7), 1633-1643

ö "Chemical Basis for the Recognition of Trimethyllysine by Epigenetic Reader Proteins"

Kamps, J.J.A.G.; Huang, J.; Poater, J.; Xu, C.; Pieters, B.J.G.E.; Dong, A.; Min, M.; Sherman, W.; Beuming, T.; Bickelhaupt, F.M.; Li, H.; Mecinović, J., Nat. Commun., 2015, 6, doi:10.1038/ncomms9911

"Initial Steps of Thermal Decomposition of Dihydroxylammonium 5,5′-bistetrazole-1,1′-diolate Crystals from Quantum Mechanics"

An, Q.; Liu, W.-G.; Goddard, W. A.; Cheng, T.; Zybin, S.V.; Xiao, H., Journal of Physical Chemistry C, 2014, 118, 27175

ö "Physics-Based Enzyme Design: Predicting Binding Affinity and Catalytic Activity"

Sirin, S.; Pearlman, D.A.; Sherman, W., Proteins, 2014, 82(12), 3397-409

ö "A Computational Approach to Enzyme Design: Predicting ω-Aminotransferase Catalytic Activity Using Docking and MM-GBSA Scoring"

Sirin, S.; Kumar, R.; Martinez, C.; Karmilowicz, M.J.; Ghosh, P.; Abramov, Y.A.; Martin, V.; Sherman, W., J. Chem. Inf. Model., 2014, 54(8), 2334-2346

ö "Lead optimization mapper: Automating free energy calculations for lead optimization"

Liu, S.; Wu, Y.; Lin, T.; Abel, R.; Redmann, J.P.; Summa, C.M.; Jaber, V.R.; Lim, N.M.; Mobley, D.L., J. Comput. Aided Mol. Des., 2013, 27(9), 755-770

ö "Type II kinase inhibitors show an unexpected inhibition mode against Parkinson’s disease-linked LRRK2 mutant G2019S"

Liu, M.; Bender, S.A.; Cuny, G.D.; Sherman, W.; Glicksman, M.; Ray, S.S, Biochemistry, 2013, 52, 1725-1736

ö "Modeling Local Structural Rearrangements Using FEP/REST: Application to Relative Binding Affinity Predictions of CDK2 Inhibitors"

Wang, L.; Deng, Y.; Knight, J.L.; Wu, Y.; Kim, B.; Sherman, W.; Shelley, J.C.; Lin, T.; Abel, R., J. Chem. Theory Comput., 2013, 9, 1282-1293

ö "Molecular determinants of selectivity and efficacy at the dopamine D3 receptor"

Newman, A.H.; Beuming, T.; Banala, A.K.; Donthamsetti, P.; Pongetti, K.; LaBounty, A.; Levy, B.; Cao, J.; Michino, M.; Luedtke, R.P.; Javitch, J.A.; Shi, L., J. Med. Chem., 2012, 55(15), 6689–6699

ö "Generation of receptor structural ensembles for virtual screening using binding site shape analysis and clustering"

Osguthorpe, D.J.; Sherman, W.; Hagler, A.T., Chem. Biol. Drug Des., 2012, 80(2), 182-193

ö "Steric hindrance mutagenesis in the conserved extracellular vestibule impedes allosteric binding of antidepressants to the serotonin transporter"

Plenge, P.; Shi, L.; Beuming, T.; Te, J.; Newman, A.H.; Weinstein, H.; Gether, U.; Loland, C.J., J. Biol. Chem., 2012, 287, 39316-39326

ö "Improving the prediction of absolute solvation free energies using the next generation OPLS force field"

Shivakumar, D.; Harder, E; Damm, W.; Friesner, R.A.; Sherman, W., J. Chem. Theory Comput., 2012, 8(8), 2553-2558

ö "Exploring protein flexibility: Incorporating structural ensembles from crystal structures and simulation into virtual screening protocols"

Osguthorpe D.J.; Sherman, W.; Hagler, A.T., J. Phys. Chem. B, 2012, 116(23), 6952-6959

"Water in the active site of ketosteroid isomerase"

Hanoian, P.; Hammes-Schiffer S., Biochemistry., 2011, 50, 6689-6700

ö "Replica Exchange with Solute Scaling: A more efficient version of Replica Exchange with Solute Tempering (REST2)"

Wang, L.; Friesner, R. A.; Berne, B. J., J. Phys Chem B., 2011, 115(30), 9431–9438

ö "Prediction of Absolute Solvation Free Energies using Molecular Dynamics Free Energy Perturbation and the OPLS Force Field"

Shivakumar, D.; Williams, J.; Wu, Y.; Damm, W.; Shelley, J.; Sherman, W., J. Chem. Theory Comput., 2010, 6, 1509–1519

ö "Probing the α-Helical Structural Stability of Stapled p53 Peptides: Molecular Dynamics Simulations and Analysis"

Guo, Z.; Mohanty, U.; Noehre, J.; Sawyer, T. K.; Sherman, W.; Krilov, G., Chem. Biol. Drug Des., 2010, 75, 348-359

ö "A Role for a Specific Cholesterol Interaction in Stabilizing the Apo Configuration of the Human A2A Adenosine Receptor"

Lyman, E.; Higgs, C.; Kim, B.; Lupyan, D.; Shelley, J. C.; Farid, R.; Voth, G. A., Structure, 2009, 17, 1660-1668

"A Conserved Protonation-Dependent Switch Controls Drug Binding in the Abl Kinase"

Shan, Y.; Seeliger, M. A.; Eastwood, M.P.; Frank, F.; Xu, H.; Jensen, M.; Dror, R.O.; Kuriyan, J.; and Shaw, D. E., Proc. Natl. Acad. Sci. USA, 2009, 106, 139-144

"Scalable Algorithms for Molecular Dynamics Simulations on Commodity Clusters"

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, Proceedings of the ACM/IEEE Conference on Supercomputing (SC06), Tampa, Florida, 2006, , November 11-17

Schrödinger has made available the set of 239 molecules used for Absolute Solvation Free Energy calculations from the following publication:

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.

Note: The downloaded file contains the set of 239 molecules in Maestro, SMILES and SMARTS format along with the experimental and calculated absolute solvation free energies.

Download the data set here

Schrödinger has also made available parameters for the set of 239 molecules used for Absolute Solvation Free Energy calculations from the following publication:

Shivakumar, D.; Harder, E.; Damm, W.; Friesner, R.A.; Sherman, W., "Improving the Prediction of Absolute Solvation Free Energies using the Next Generation OPLS Force Field,"  J. Chem. Theory Comput., 2012, 8, 2553-2558.

Note: The OPLS 2.0 parameters used in the above study were generated using Schrödinger Suite 2011. Parameters and structures for acenaphthylene (compound m78) and acenaphthene (compound m78_new) are also available for download below.

Download the OPLS 2.0 parameters file here
Download the parameters file for acenaphthylene and acenaphthene here
Download the structures for acenaphthylene and acenaphthene here

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. Desmond's high-performance Molecular Dynamics code, together with continuously improving computer hardware technologies, are helping scientists make progress toward key goals.

Figure 1: demonstrating a range of over 10x to 64x increase in performance (ns/day) compared to standard 4 and 8 CPU processors.

*Per Single GPU

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
  • Intel Core or Xeon CPU
  • NVIDIA Tesla GPU
  • Latest Schrödinger Software Release preinstalled, tested, and optimized
  • CentOS 7
Learn more
MD/FEP+ Servers
  • Intel Xeon E5-2600 v3 CPU
  • NVIDIA Tesla GPU
  • Latest Schrödinger Software Release preinstalled, tested, and optimized
  • CentOS 7
Learn more

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
  • Available in the United States


  • 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.

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