Predicting Hydration Free Energies of the FreeSolv Database of Drug-like Molecules with Molecular Density Functional Theory.,Journal of Chemical Information and Modeling

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Predicting Hydration Free Energies of the FreeSolv Database of Drug-like Molecules with Molecular Density Functional Theory.
Journal of Chemical Information and Modeling ( IF 5.6 ) Pub Date : 2020-06-25 , DOI: 10.1021/acs.jcim.0c00526
Sohvi Luukkonen ₁ , Luc Belloni ₂ , Daniel Borgis _{1,

3} , Maximilien Levesque _{3,

4}

Affiliation

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.

中文翻译：

用分子密度泛函理论预测类药物分子的FreeSolv数据库的水合自由能。

我们评估分子密度泛函理论（MDFT）的性能，以预测小类药物分子基准FreeSolv的水合自由能。超网状链逼近（HNC）中的MDFT与压力校正相结合，可预测FreeSolv数据库的实验水合自由能在1 kcal / mol之内，每分子平均计算时间为2 cpu·min。这与基于仿真的自由能计算的精度相同，后者通常每个分子需要数百cpu·h或数十gpu·h。

更新日期：2020-07-27

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