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Predicting Hydration Free Energies of the FreeSolv Database of Drug-like Molecules with Molecular Density Functional Theory

Sohvi Luukkonen 1 Luc Belloni 2 Daniel Borgis 1, 3 Maximilien Levesque 3
1 MDLS - Maison de la Simulation
2 LIONS - Laboratoire Interdisciplinaire sur l'Organisation Nanométrique et Supramoléculaire
NIMBE UMR 3685 - Nanosciences et Innovation pour les Matériaux, la Biomédecine et l'Energie (ex SIS2M)
3 PASTEUR - Processus d'Activation Sélective par Transfert d'Energie Uni-électronique ou Radiatif (UMR 8640)
Abstract : We assess the performance of molecular density functional theory (MDFT) to predict hydration free energies of the small drug-like molecules benchmark, FreeSolv. The MDFT in the hypernetted chain approximation (HNC) coupled with a pressure correction predicts experimental hydration free energies of the FreeSolv database within 1 kcal/mol with an average computation time of 2 cpu·min per molecule. This is the same accuracy as for simulation-based free energy calculations that typically require hundreds of cpu·h or tens of gpu·h per molecule.
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https://hal-cea.archives-ouvertes.fr/cea-02909000
Contributor : Serge Palacin <>
Submitted on : Wednesday, July 29, 2020 - 5:21:18 PM
Last modification on : Wednesday, November 4, 2020 - 3:34:54 AM

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CEA | CNRS | DSM-IRAMIS | UVSQ | PUBNIMBE | IRAMIS-NIMBE | INRIA | CEA-UPSAY | MDLS | INC-CNRS | CEA-DRF | SORBONNE-UNIVERSITE | PSL

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Sohvi Luukkonen, Luc Belloni, Daniel Borgis, Maximilien Levesque. Predicting Hydration Free Energies of the FreeSolv Database of Drug-like Molecules with Molecular Density Functional Theory. Journal of Chemical Information and Modeling, American Chemical Society, 2020, 60 (7), pp.3558-3565. ⟨10.1021/acs.jcim.0c00526⟩. ⟨cea-02909000⟩

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