FEP+

High-performance free energy calculations for drug discovery

Discover better quality molecules, faster with FEP+

FEP+ is Schrödinger’s proprietary, physics-based free energy perturbation technology for computationally predicting protein-ligand binding at an accuracy matching experimental methods, across broad chemical space.

Explore vast chemical space and reduce costs
Leverage FEP+ as an accurate, digital binding affinity assay to drive rapid in silico design cycles and focus experimental efforts on only the highest quality ideas.

Improve molecular profiles, efficiently 
Optimize multiple properties simultaneously, including potency, selectivity, and solubility, to improve the profile and developability of small and large molecules.

Pursue novel chemistry with confidence
Synthesize novel and challenging chemistry with a high degree of confidence through prospective application of FEP+.

Automated FEP+ Workflows to Drive Real-world Discovery

Access a diverse set of FEP+ workflows that cover design scenarios common in drug discovery programs: 

  • Predict change of affinity and selectivity for structural modifications of small molecules
  • Score diverse small molecules to enrich hits in virtual screens 
  • Predict ligand kinetic and thermodynamic solubility
  • Facilitate macrocyclization and fragment linking to improve affinity and selectivity
  • Predict selectivity through protein residue mutation between on and off targets
  • Engineer biologics for affinity and stability

State-of-the-art Force Field:

The OPLS4 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 OPLS4 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 OPLS4 force field to achieve high accuracy in the modeling of protein dynamics, small molecule solvation and small conformational energetics, and protein-ligand binding.

Intuitive GUI:

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.

Enhanced Sampling:

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.

GPU-enabled:

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 2022-4: FEP+, Schrödinger, New York, NY, 2021.

ö Tang, H.; Jensen, K.; Houang, E.; McRobb, F. M.; Bhat, S.; Svensson, M.; Bochevarov, A.; Day, T.; Dahlgren, M. K.; Bell, J. A.; et al., “Discovery of a Novel Class of D-amino Acid Oxidase (DAO) Inhibitors with the Schrödinger Computational Platform,” J. Med. Chem., 2022, 65(9), 6775-6802

ö Özen, A.; Perola, E.; Brooijmans, N.; Kim, J., “Prospective Applications of Free Energy Methods in Drug Discovery Programs,” Free Energy Methods in Drug Discovery: Current State and Future Directions, 2021, Chapter 5, 127-141

ö Abel, R.; Wang, L.; Harder, E.D.; Berne, B.J.; Friesner, R.A., "Advancing Drug Discovery through Enhanced Free Energy Calculations," Acc. Chem. Res., 2017, 50 (7), 1625-1632

ö Scarabelli, G.; Oloo, E. O.; Maier, J. K. X.; Rodriguez-Granillo, A., “Accurate Prediction of Protein Thermodynamic Stability Changes Upon Residue Mutation Using Free Energy Perturbation,” JMB., 2022, 434, 1673-75

ö Chen, W.; Cui, D.; Jerome, S. V.; Michino, M.; Lenselink, E. B.; Huggins, D.; Beautrait, A.; Vendome, J.; Abel, R.; Friesner, R. A.; Wang, L., “Enhancing Hit Discovery in Virtual Screening Through Accurate Calculation of Absolute Protein-Ligand Binding Free Energies,” ChemRxiv., 2022, preprint

ö Kuhn, B.; Tichý, M.; Wang, L.; Robinson, S.; Martin, R.E.; Kuglstatter, A.; Benz, J.; Giroud, M., Schirmeister, T.; Abel, R.; Diederich, F.; Hert, J., "Prospective Evaluation of Free Energy Calculations for the Prioritization of Cathepsin L Inhibitors," J. Med. Chem., 2017, 60 (6), 2485-2497

ö Yu, H.S.; Deng, Y.; Wu, Y.; Sindhikara, D.; Rask, A.R.; Kimura, T.; Abel, R.; Wang, L., "Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets," J. Chem Theory Comput., 2017, 13 (12), 6290-6300

ö Wang, L.; Deng, Y.; Wu, Y.; Kim, B.; LeBard, D.N.; Wandschneider, D.; Beachy, M.; Friesner, R.A; Abel, R., "Accurate Modeling of Scaffold Hopping Transformations in Drug Discovery," J. Chem Theory Comput., 2017, 13 (1), 42-54

ö 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., "OPLS3: A Force Field Providing Broad Coverage of Drug-like Small Molecules and Proteins," J. Chem. Theory Comput., 2016, 2 (1), 281–296

ö Wang, L.; Wu, Y.; Deng, Y.; Kim, B.; Pierce, L.; Krilov, G.; Lupyan, D.; Robinson, S.; Dahlgren, M.K.; Greenwood, J.; et al., "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," J. Am. Chem. Soc., 2015, 137 (7), 2695–270

 

· "Discovery of a Novel Class of d-Amino Acid Oxidase Inhibitors Using the Schrödinger Computational Platform"

Haifeng Tang, et al., Journal of Medicinal Chemistry, 2022, 65(9), 6775-6802

· "Induced-Fit Docking Enables Accurate Free Energy Perturbation Calculations in Homology Models"

Tianchuan Xu, Kai Zhu, Alexandre Beautrait, Jeremie Vendome, Kenneth Borrelli, Robert Abel, Richard Friesner, Edward Miller , ChemRxiv, 2022, Preprint,

· "Exploring the Activity Profile of TbrPDEB1 and hPDE4 Inhibitors Using Free Energy Perturbation"

Lorena Zara, Francesca Moraca, Jacqueline E. Van Muijlwijk-Koezen, Barbara Zarzycka, Robert Abel, and Iwan J. P. de Esch, ACS Medicinal Chemistry Letters, 2022, 13(6), 904-910

· "Accurate Prediction of Protein Thermodynamic Stability Changes upon Residue Mutation using Free Energy Perturbation"

Guido Scarabelli, Eliud O. Oloo, Johannes K.X. Maier, Agustina Rodriguez-Granillo, JMB, 2022, 434(2),

· "The Impact of Experimental and Calculated Error on the Performance of Affinity Predictions"

Gary Tresadern, Kanaka Tatikola, Javier Cabrera, Lingle Wang, Robert Abel, Herman van Vlijmen, and Helena Geys, JCIM, 2022, 62(3), 703-717

"Are Deep Learning Structural Models Sufficiently Accurate for Free-Energy Calculations? Application of FEP+ to AlphaFold2-Predicted Structures"

Thijs Beuming, Helena Martín, Anna M. Díaz-Rovira, Lucía Díaz, Victor Guallar, and Soumya S. Ray, Journal of Chemical Information and Modeling, 2022, 62(18), 4351-4360

· "AutoDesigner, a De Novo Design Algorithm for Rapidly Exploring Large Chemical Space for Lead Optimization: Application to the Design and Synthesis of D-Amino Acid Oxidase Inhibitors"

Pieter H. Bos, Evelyne M. Houang, Fabio Ranalli, Abba E. Leffler, Nicholas A. Boyles, Volker A. Eyrich, Yuval Luria, Dana Katz, Haifeng Tang, Robert Abel, and Sathesh Bhat, JCIM, 2022, 62(8), 1905-1915

· "From computer-aided drug discovery to computer-driven drug discovery"

Leah Frye, Sathesh Bhat, Karen Akinsanya, Robert Abel, Drug Discovery Today: Technologies, 2021, 39, 111-117

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

Dana Katz, Michael A. DiMattia, Dan Sindhikara, Hubert Li, Nikita Abraham, Abba E. Leffler, Marine Drugs, 2021, 19(7), 367

· "Impacting Drug Discovery Projects with Large-Scale Enumerations, Machine Learning Strategies, and Free-Energy Predictions"

Jennifer L. Knight, Karl Leswing, Pieter H. Bos, and Lingle Wang, Free Energy Methods in Drug Discovery, 2021, 1397, 205-226

· "Potency-Enhancing Mutations of Gating Modifier Toxins for the Voltage-Gated Sodium Channel NaV1.7 Can Be Predicted Using Accurate Free-Energy Calculations"

Katz, D.; Sindhikara, D.; DiMattia, M.; Leffler, A.E., Toxins, 2021, 13(3), 193

· "General Theory of Fragment Linking in Molecular Design: Why Fragment Linking Rarely Succeeds and How to Improve Outcomes"

Yu, HS.; Modugula, K.; Ichihara, O.; Kramschuster, K.; Keng, S.; Abel, R.; Wang, L., J. Chem. Theory Comput., 2021, 17 (1), 450–462

· "A Free Energy Perturbation Approach to Estimate the Intrinsic Solubilities of Drug-like Small Molecules"

Mondal, S; Tresadern, G.; Greenwood, J.; Kim, B.; Kaus, J.; Wirtala, M.; Steinbrecher, T.; Wang, L.; Masse, C.; Farid, R.; Abel, R., ChemRxiv., 2020, preprint, https://doi.org/10.26434/chemrxiv.10263077.v1

· "Advancing Free-Energy Calculations of Metalloenzymes in Drug Discovery via Implementation of LFMM Potentials"

Dajnowicz, S.; Ghoreishi, D.; Modugula, K.; Damm, W.; Harder, E.D.; Abel, R.; Wang, L.; Yu, H.S., J. Chem. Theory Comput., 2020, X, X-X

· "A Reliable and Accurate Solution to the Induced Fit Docking Problem for Protein-Ligand Binding"

Miller, E.; Murphy, R.; Sindhikara, D.; Borrelli, K.; Grisewood, M.; Ranalli, F.; Dixon, S.; Jerome, S.; Boyles, N.; Day, T.; Ghanakota, P.; Mondal, S.; Rafi, S.B.; Troast, D.M.; Abel, R.; Friesner, R.A., ChemRxiv, 2020, Preprint, 1

· "Impact of Different Automated Binding Pose Generation Approaches on Relative Binding Free Energy Simulations"

Cappel, D.; Jerome, S.; Hessler, G.; Matter, H., J. Chem. Inf. Model., 2020, 60 (3), 1432-1444

· "Is structure based drug design ready for selectivity optimization?"

Albanese, S.K.; Chodera, J.D.; Volkamer, A.; Keng, S.; Abel, R.; Wang, L., bioRxiv, 2020, Pre Print, 1-35

· "Combining Cloud-Based Free Energy Calculations, Synthetically Aware Enumerations and Goal-Directed Generative Machine Learning for Rapid Large-Scale Chemical Exploration and Optimization"

Ghanakota, P.; Bos, P.; Konze, K.; Staker, J.; Marques, G.; Marshall, K.; Leswing, K.; Abel, R.; Bhat, S., ChemRxiv, 2020, Preprint, xx-xx

"Enhancing Water Sampling in Free Energy Calculations with Grand Canonical Monte Carlo"

Ross, G.A.; Russell, E.; Deng, Y.; Lu, C.; Harder, E.D.; Abel, R.; Wang, L.;, J. Chem. Theory Comput., 2020, X,

· "Large-Scale Assessment of Binding Free Energy Calculations in Active Drug Discovery Projects"

Schindler, C.; Baumann, H.; Blum, A.; Böse, D.; Buchstaller, H-P.; Burgdorf, L.; et al. , J. Chem. Inf. Model., 2020, Preprint, xx-xx

· "Digitalisierung: molekulares Design plattformisieren"

Scarbath-Evers, K.; Cappel, D.; Weiser, J., Nachrichten aus der Chemie, 2020, Jul (68), 34-36

"Evaluation of Free Energy Calculations for the Prioritization of Macrocycle Synthesis"

Paulsen, J.L.; Yu, H.S.; Sindhikara, D.; Wang, L.; Appleby, T.C.; Villaseñor, A.G.; Schmitz, U.; Shivakumar, D., J. Chem. Inf. Model., 2020, 60 (7), 3489–3498

· "Discovery of Potent, Selective, and Orally Bioavailable Inhibitors of USP7 with In Vivo Anti-Tumor Activity"

Leger, P.R.; Hu, D.X.; Biannic, B.; Bui, M.; Han, X.; et. al., J. Med. Chem., 2020, 63 (10), 5398–5420

· "Noncovalent inhibitors reveal BTK gatekeeper and auto-inhibitory residues that control its transforming activity "

Wang, S; Mondal, S; Zhao, C; Berishaj, M; Ghanakota, P; Batlevi, CL; Dogan, A; Seshan, VE, Abel, R; Green, MR; Younes, A; Wendel, H-G, JCI Insight, 2019, 4(12), e127566

· "Application of Free Energy Perturbation (FEP+) to Understanding Ligand Selectivity: A Case Study to Assess Selectivity Between Pairs of Phosphodiesterases (PDE’s)"

Moraca, F.; Negri, A.; de Oliveira, C.; Abel, R., J. Chem. Inf. Model., 2019, 59(6), 2729-2740

· "Reaction-based Enumeration, Active Learning, and Free Energy Calculations to Rapidly Explore Synthetically Tractable Chemical Space and Optimize Potency of Cyclin Dependent Kinase 2 Inhibitors"

Konze, K.; Bos, P.; Dahlgren, M.; Leswing, K.; Tubert-Brohman, I.; Bortolato, A.; Robbason, B.; Abel, R.; Bhat, S., J. Chem. Inf. Model., 2019, 59, 9, 3782–3793

· "Relative Binding Affinity Prediction of Charge-Changing Sequence Mutations with FEP in Protein–Protein Interfaces"

Clark, A.J.; Negron, C.; Hauser, K.; Sun, M.; Wang, L.; Abel, R.; Friesner, R.A., Journal of Molecular Biology, 2019, ,

· "Rigorous Free Energy Perturbation Approach to Estimating Relative Binding Affinities between Ligands with Multiple Protonation and Tautomeric States"

Oliveira, C; Yu, HS; Chen, W; Abel, R; Wang, L, J. Chem. Theory Comput., 2019, 15 (1), 424-435

· "Protein–Ligand Binding Free Energy Calculations with FEP+"

Wang, L; Chambers, J; Abel, R, Biomolecular Simulations, 2019, 2022, 201-232

"Computational Macrocyclization: From de novo Macrocycle Generation to Binding Affinity Estimation"

Wagner, V.; Jantz, L.; Briem, H.; Sommer, K.; Rarey, M.; Christ, C., ChemMedChem, 2018, 12, 1866-1872

· "Prediction of Accurate Binding Modes Using Combination of Classical and Accelerated Molecular Dynamics and Free-Energy Perturbation Calculations: An Application to Toxicity Studies"

Fratev, F.; Steinbrecher, T.B.; Jónsdóttir, S.O., ACS Omega, 2018, 3(4), 4357–4371

"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

· "The Role of Bridging Water and Hydrogen Bonding as Key Determinants of Noncovalent Protein–Carbohydrate Recognition"

Ruvinsky, A.M.; Aloni, I.; Cappel, D.; Higgs, C.; Marshall, K.; Rotkiewicz, P.; Repasky, M.; Feher, V.A.; Feyfant, E.; Hessler, G.; Matter, H., ChemMedChem, 2018, ,

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

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

· "A Critical Review of Validation, Blind Testing, and Real-World Use of Alchemical Protein-Ligand Binding Free Energy Calculations"

Abel, R.; Wang, L.; Mobley, D.L.; Friesner, R., Curr Top Med Chem., 2017, 17, 1-9

· "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

· "Advancing Drug Discovery through Enhanced Free Energy Calculations"

Abel, R.; Wang, L.; Harder, E.D.; Berne, B.J.; Friesner, R.A., Acc. Chem. Res., 2017, 50, 1625-1632

"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

"Best Practices of Computer-Aided Drug Discovery: Lessons Learned from the Development of a Preclinical Candidate for Prostate Cancer with a New Mechanism of Action"

Ban, F.; Dalal, K.; Li, H.; LeBlanc, E.; Rennie, P.S.; Cherkasov, A., J. Chem. Inf. Model., 2017, 57(5), 1018–1028

"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

· "Free Energy Perturbation Calculations of the Thermodynamics of Protein Side-Chain Mutations"

Steinbrecher, T.; Abel, R.; Clark, A.; Friesner, R., J. Mol. Biol. , 2017, 429 (7), 923–929

· "Accurate and Reliable Prediction of the Binding Affinities of Macrocycles to Their Protein Targets"

Yu, H. S.; Deng, Y.; Wu, Y.; Sindhikara, D.; Rask, A. R.; Kimura, T.; Abel, R.; Wang, L., J. Chem. Theory Comput., 2017, 13(12), 6290–6300

· "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

· "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

· "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

· "Free Energy Calculation Guided Virtual Screening of Synthetically Feasible Ligand R-Group and Scaffold Modifications: An Emerging Paradigm for Lead Optimization"

Abel, R.; Bhat, S., Annual Reports in Medicinal Chemistry, 2017, 50, 237 - 262

· "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, 60 (6), 2485–2497

· "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

· "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

· "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

· "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

"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

· "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

· "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

· "On the Rational Design of Zeolite Clusters"

Migues, A.N.; Muskat, A.; Auerbach, S.M.; Sherman, W.; Vaitheeswaran, S., ACS Catal., 2015, 5, 2859-2865

· "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.; Greenwood, J.; Romero, D.; Masse, C.; Knight, J.; Steinbrecher, T.; Beuming, T.; Damm, W.; Harder, E.; Sherman, W.; Brewer, M.; Wester, R.; et al., J. Am. Chem. Soc., 2015, 137, 2695-2703

· "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

· "Pyrido[4,3‑e][1,2,4]triazolo[4,3‑a]pyrazines as Selective, Brain Penetrant Phosphodiesterase 2 (PDE2) Inhibitors"

Rombouts, F.J.R.; Tresadern, G.; Buijnsters, P.; Langlois, X.; Tovar, F.; Steinbrecher, T. B.; Vanhoof, G.; Somers, M.; Andrés, J.; Trabanco, A. A., ACS Med. Chem. Lett., 2015, 6(3), 282–286

· "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

· "On achieving high accuracy and reliability in the calculation of relative protein–ligand binding affinities"

Wang, L.; Berne, B. J.; Friesner, R. A., Proc Natl Acad Sci U S A., 2012, 109(6), 1937-1942

· "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

· "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

· "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

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: In multiple runs of over 23k - 93k atoms, various GPUs demonstrated a range of over 10x to 64x increase in performance (ns/day) compared to standard 4 and 8 CPU processors.

*Per Single GPU

Figure 2Scaling across various GPU cards when running FEP+ BACE1 tutorial. Higher end cards showed a 2.7x increase in perturbations per day per 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 Suite preinstalled, tested, and optimized
  • CentOS 7
Learn more
MD/FEP+ Servers
  • Intel Xeon E5-2600 v3 CPU
  • NVIDIA Tesla GPU
  • Latest Schrödinger Software Suite 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

 

Advantages

  • 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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