software
Use Flare FEP to make the right ligand design choices and enable lead optimization with confidence
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 hundreds or thousands of compounds, eliminating the time wasted on synthesizing non-potent molecules. Benchmarking FEP 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. Then production FEP experiments yield predicted binding free energies for new molecular designs.
FEP calculation results typically fall within 1kcal/ mol from 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 significantly different from the initial molecular structure and with enhanced potency.
Run an FEP experiment starting from scratch with a few simple steps, thanks to the platform’s user-friendly interface
Comparison of Flare FEP's accuracy in relation to previous versions of Flare (depicted in shades of blue) and the results of Wang et al. (illustrated in purple)
Contact us to request a free evaluation of Flare FEP, or to learn more about our licensing options
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.org, Sire 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 Study, J. 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
L. Wang, Y. Wu, Y. Deng, B. Kim, L. Pierce, G. Krilov, D. Lupyan, S. Robinson, M. K. Dahlgren, J. Greenwood, D. L. Romero, C. Masse, J. L. Knight, T. Steinbrecher, T. Beuming, W. Damm, E. Harder, W. Sherman, M. Brewer, R. Wester, M. Murcko, L. Frye, R. Farid, T. Lin, D. L. Mobley, W. L. Jorgensen, B. J. Berne, R. A. Friesner, R. Abel, 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, 2695−2703