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 (relative binding FEP+)
  • Score diverse small molecules to enrich hits in virtual screens (absolute binding FEP+)
  • Predict ligand kinetic and thermodynamic solubility (FEP+ solubility)
  • Facilitate macrocyclization and fragment linking to improve affinity and selectivity
  • Predict selectivity through protein residue mutation between on and off targets (protein FEP+)
  • Engineer biologics for affinity and stability (protein FEP+)

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 2023-4: FEP+, Schrödinger, New York, NY, 2023.

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

 

DOWNLOAD FEP+ FLYER

"Scaffold Hopping and Optimization of Small Molecule Soluble Adenyl Cyclase Inhibitors Led by Free Energy Perturbation"

Shan Sun, Makoto Fushimi, Thomas Rossetti, Navpreet Kaur, Jacob Ferreira, Michael Miller, Jonathan Quast, Joop van den Heuvel, Clemens Steegborn, Lonny R. Levin, Jochen Buck, Robert W. Myers, Stacia Kargman, Nigel Liverton, Peter T. Meinke, and David J. H, Journal of Chemical Information and Modeling, 2023, ,

· "Target-template relationships in protein structure prediction and their effect on the accuracy of thermostability calculations"

Muyun Lihan, Dmitry Lupyan, Daniel Oehme, Protein Science, 2023, 32(2),

· "FEP Protocol Builder: Optimization of Free Energy Perturbation Protocols Using Active Learning"

César de Oliveira, Karl Leswing, Shulu Feng, René Kanters, Robert Abel, and Sathesh Bhat, Journal of Chemical Information and Modeling, 2023, ,

· "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"

Dilek Coskun, Muyun Lihan, Joao Rodrigues, Marton Vass, Daniel Robinson, Richard Friesner, Edward Miller, ChemRxiv Theoretical and Computational Chemistry, 2023, Preprint,

· "Enhancing Hit Discovery in Virtual Screening through Absolute Protein–Ligand Binding Free-Energy Calculations"

Wei Chen, Di Cui, Steven V. Jerome, Mayako Michino, Eelke B. Lenselink, David J. Huggins, Alexandre Beautrait, Jeremie Vendome, Robert Abel, Richard A. Friesner, and Lingle Wang, Journal of Chemical Information and Modeling, 2023, ,

· "Reliable and Accurate Prediction of Single-Residue pKa Values through Free Energy Perturbation Calculations"

Dilek Coskun, Wei Chen, Anthony J. Clark, Chao Lu, Edward D. Harder, Lingle Wang, Richard A. Friesner, and Edward B. Miller, Journal of Chemical Theory and Computation, 2022, 18(12), 7193–7204

· "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

· "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 , Journal of Chemical Theory and Computation, 2022, 18(9), 5710–5724

· "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

· "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

· "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

· "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

· "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

· "Novel Physics-Based Ensemble Modeling Approach That Utilizes 3D Molecular Conformation and Packing to Access Aqueous Thermodynamic Solubility: A Case Study of Orally Available Bromodomain and Extraterminal Domain Inhibitor Lead Optimization Series"

Richard S. Hong, Alessandra Mattei, Ahmad Y. Sheikh, Rajni Miglani Bhardwaj, Michael A. Bellucci, Keith F. McDaniel, M. Olivia Pierce, Guangxu Sun, Sizhu Li, Lingle Wang, Sayan Mondal, Jianguo Ji, and Thomas B. Borchardt, Journal of Chemical Information and Modeling, 2021, 61(3), 1412-1426

· "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

· "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

· "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

· "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 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

· "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

· "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

"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,

· "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

· "Digitalisierung: molekulares Design plattformisieren"

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

· "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

"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

· "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

· "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, ,

· "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

· "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, ,

"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

"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

· "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

· "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

· "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

· "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

· "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

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

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

"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

· "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

· "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

"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

· "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

· "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

"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

· "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

"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

· "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

· "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

· "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

· "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

· "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

· "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

· "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

· "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

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.

FEP+ Performance Graph

Figure 2: Scaling across various GPU cards when running FEP+ BACE1 tutorial using Schrödinger Suite 2023-1.

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.