FEP+

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 OPLS3e 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 OPLS3e 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 OPLS3e 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.

Error Estimates:
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.

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

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 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

ö "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

"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, ,

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

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

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ö "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

ö "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

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

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

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ö "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

ö "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

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

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ö "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

ö "Digitalisierung: molekulares Design plattformisieren"

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ö "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

ö "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

ö "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

ö "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, ,

ö "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

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

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

ö "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

"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

ö "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

ö "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, ,

ö "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

"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

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

"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 "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

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

"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

ö "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

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 "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

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

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

ö "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

ö "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

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

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 "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 "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

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.; 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

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 "On the Rational Design of Zeolite Clusters"

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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"

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ö "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

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 "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

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

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

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

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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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