Computational Fluorine Scanning Using Free-Energy Perturbation

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Wade, Alexander David 
Rizzi, Andrea 
Wang, Yuanqing 
Huggins, David John 

We present perturbative fluorine scanning, a computational fluorine scanning approach using free-energy perturbation. This method can be applied to molecular dynamics simulations of a single compound and make predictions for the best binders out of numerous fluorinated analogues. We tested the method on nine test systems: Renin, DPP4, Menin, P38, Factor Xa, CDK2, AKT, JAK2, and Androgen Receptor. The predictions were in excellent agreement with more rigorous alchemical free-energy calculations and in good agreement with experimental data for most of the test systems. However, the agreement with experiment was very poor in some of the test systems and this highlights the need for improved force fields in addition to accurate treatment of tautomeric and protonation states. The method is of particular interest due to the wide use of fluorine in medicinal chemistry to improve binding affinity and ADME properties. The promising results on this test case suggest that perturbative fluorine scanning will be a useful addition to the available arsenal of free-energy methods.

Chemistry, Pharmaceutical, Drug Design, Fluorine, Hydrogen, Molecular Conformation, Molecular Dynamics Simulation, Thermodynamics
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Journal of Chemical Information and Modeling
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American Chemical Society (ACS)
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Engineering and Physical Sciences Research Council (EP/L015552/1)
Engineering and Physical Sciences Research Council (EP/P020259/1)
EPSRC (1819407)
Medical Research Council (MR/L007266/1)
Work in the D.J.H. laboratory was supported by the Medical Research Council under grant ML/L007266/1. A.D.W. would like to acknowledge the EPSRC Centre for Doctoral Training in Computational Methods for Materials Science for funding under grant number EP/L015552/1. A.R. would like to acknowledge John Chodera (ORCID 0000-0003-0542-119X) for support and enlightening discussions and suggestions that are reflected in this manuscript. A.R. also acknowledges partial support from the Sloan Kettering Institute and the Tri-Institutional Program in Computational Biology and Medicine. All calculations were performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http:// and were funded by the EPSRC under grant EP/P020259/1.