Free Energy Perturbation (FEP)

Use Flare FEP to refine your docking results, make the right structure-based design choices and enable lead optimization with confidence

Robust and efficient calculations

  • Seamlessly prepare proteins and ligands for the FEP experiment
  • Easily generate ligand poses with the ligand alignment component of Flare, or by docking ligands to the protein you will use in Flare FEP project
  • Use the automated process to create an efficient perturbation network, automatically including intermediate ligands when needed
  • Multiple options to refine and optimize the perturbation map
  • Easily view and troubleshoot all aspects of your Flare FEP calculation results in a dedicated window and identify problematic transformations
  • View, sort and prioritize your ligands using their predicted activity

Accurate FEP calculations

  • Accurately calculate the relative binding free energy of a closely related (or congeneric) series of ligands (Assessment of Binding Affinity via Alchemical Free-Energy Calculations J. Chem. Inf. Model. 2020, 60, 6, 3120–3130)
  • Precision is obtained in your Flare FEP calculations using fully connected, two-way networks, which enable more accurate error estimation for each link and thorough analysis of cycle errors
  • Enhance predictive performance by automatically creating custom torsion parameters for small molecules using GFN2-xTB extended semiempirical tight-binding or ANI-2x deep learning QM approximation
  • Easily generate network graphs by adding automated intermediates to better link structurally related ligands

Free energy cycle

Free energy cycle

Faster FEP calculations

  • Split your Flare FEP calculations into the optimal package size for your GPU resources, enabling single compounds to be predicted in as little as 30 minutes
  • Flare FEP uses an advanced integrator, including hydrogen mass repartitioning and a more optimal water box shape and size for the simulation
  • Optimize calculation time by using the Cresset Engine Broker™ to connect to a local cluster or cloud computing facilities
  • Achieve a more tailored, and quicker, simulation as assignment of the number of λ windows to use between each perturbation is determined by the software adaptively and automatically

Flare V5 FEP speedup overV4

Flare FEP calculations are 2-3.5 times faster in Flare V5 compared to Flare V4

References and acknowledgements

P. Eastman, J. Swails, J. D. Chodera, R. T. McGibbon, Y. Zhao, K. A. Beauchamp, L.-P. Wang, A. C. Simmonett, M. P. Harrigan, C. D. Stern, R. P. Wiewiora, B. R. Brooks, and V. S. Pande, OpenMM 7: Rapid development of high performance algorithms for molecular dynamics, PLOS Comp. Biol. 2017, 3(7): e1005659

S. Liu, Y. Wu, T. Lin, R. Abel, J. P. Redmann, C. M. Summa, V. R. Jaber, N. M. Lim, D. L. Mobley, Lead optimization mapper: automating free energy calculations for lead optimization, J. Comput. Aided Mol. Des. 2013, 27, 9, 755-770

L. O. Hedges, A. S. J. S Mey, , C. A. Laughton, F. L. Gervasio, A. J. Mulholland,C. J. Woods, J. Michel, BioSimSpace: An interoperable Python framework for biomolecular simulation, Journal of Open Source Software 2019, 4(43), 1831

C. Woods , L. O. Hedges, A. S. J. S. Mey , G. Calabrò and J. Michel , www.siremol.orgSire Molecular Simulation Framework , 2022

G. Calabrò , C. J. Woods , F. Powlesland , A. S. J. S. Mey , A. J. Mulholland and J. Michel , Elucidation of Nonadditive Effects in Protein–Ligand Binding Energies: Thrombin as a Case StudyJ. Phys. Chem. B, 2016,  120 , 5340 —5350 

H. H. Loeffler, J. Michel, C. J. Woods, FESetup: Automating Setup for Alchemical Free Energy Simulations, J. Chem. Inf Model. 2015, 55 (12), 2485-2490

C. Devereux, J. S. Smith, K. K. Huddleston, K. Barros, R. Zubatyuk, O. Isayev, and A. E. Roitberg, Extending the applicability of the ANI deep learning molecular potential to sulfur and halogens,  J. Chem. Theory Comput. 2020, 16,7, 4192–4202

Flare FEP resources


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