Molecular visualization in Flare Version 6 background


Flare™ Free Energy Perturbation (FEP)

Use Flare FEP to make the right ligand design choices and enable lead optimization with confidence

Flare FEP™: Competitively Combining Speed and Accuracy with Ease of Use

A robust and accurate method for calculating binding affinity

Flare Free Energy Perturbation (FEP) offers a quantitative method to reliably calculate relative binding affinity, enabling accurate ranking of molecules in a congeneric ligand series. The method enables users to test 'in-silico' a large number of molecules, prior to focusing on ‘wet’ lab work, meaning that fewer compounds need to be made and tested to achieve the desired results.

The process supports chemists in making known actives more potent, without having to synthesize 100s or 1000s of compounds, eliminating the time wasted on non-potent molecules. FEP projects can begin with a much smaller number of ligands than may be required for other methods. Benchmarking experiments can be used to gain confidence that your system is prepared correctly, confirming the predictivity of the method on the target and ligand series of interest.

FEP calculation results typically fall within 1kcal/ mol reach of experimental data. This means that Flare users can make better, accurately informed decisions around which ligand modifications can achieve the best results, with new molecule designs often ending up quite far away from the initial starting point.

  • Reduce the amount of ‘wet’ chemistry across all stages of drug discovery
  • Start with a smaller number of ligands than typically required for other computational methods
  • Generate reliable, ranked values to represent ligand binding affinity
  • Reduce the amount of time spent synthesizing or analyzing non-potent compounds
  • Make better decisions about where to move next within your structure-based design project

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 improving the water sampling of occluded pockets in the protein active site using Grand Canonical Nonequilibrium Candidate Monte Carlo (GCNCMC)
  • Enhance predictive performance by automatically creating custom torsion parameters for small molecules using GFN2-xTB extended semiempirical tight-binding, ANI-2x deep learning QM approximation or hybrid DFT//GFN2-xTB calculations
  • 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 and onwards 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

M. L. Samways, H. E. Bruce Macdonald, J. W. Essex, grand: A Python Module for Grand Canonical Water Sampling in OpenMM, J. Chem. Inf. Model. 2020, 60, 10, 4436-4441

O. J. Melling, M. L. Samways, Y. Ge, D. L. Mobley, J. W. Essex, Enhanced Grand Canonical Sampling of Occluded Water Sites Using Nonequilibrium Candidate Monte Carlo, J. Chem. Theory Comput. 2023, 19, 1050-1062

C. Bannwarth, S. Ehlert, S. Grimme, GFN2-xTB—An accurate and broadly parametrized self-consistent tight-binding quantum chemical method with multipole electrostatics and density-dependent dispersion contributions, J. Chem. Theory Comp. 2019, 15, 3, 1652-1671

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

P. K. Behara, H. Jang, J. Horton, D. Dotson, S. Boothroyd, C. Cavender, V. Gapsys, T. Gokey, D. Hahn, J. Maat, O. Madin, I. Pulido, M. Thompson, J. Wagner, L.  Wang, J.  Chodera, D. Cole, M. Gilson, M. Shirts, C.  Bayly, L.-P.  Wang, D.  Mobley, Benchmarking QM theory for drug-like molecules to train force fields, Poster presented at CUP XXI, March 08, 2022 - March 10, 2022, Santa Fe, New Mexico, USA

Flare FEP resources


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