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SAMPL6 blind predictions of water-octanol partition coefficients using nonequilibrium alchemical approaches

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Abstract

In this paper, we compute, by means of a non equilibrium alchemical technique, the water-octanol partition coefficients (LogP) for a series of drug-like compounds in the context of the SAMPL6 challenge initiative. Our blind predictions are based on three of the most popular non-polarizable force fields, CGenFF, GAFF2, and OPLS-AA and are critically compared to other MD-based predictions produced using free energy perturbation or thermodynamic integration approaches with stratification. The proposed non-equilibrium method emerges has a reliable tool for LogP prediction, systematically being among the top performing submissions in all force field classes for at least two among the various indicators such as the Pearson or the Kendall correlation coefficients or the mean unsigned error. Contrarily to the widespread equilibrium approaches, that yielded apparently very disparate results in the SAMPL6 challenge, all our independent prediction sets, irrespective of the adopted force field and of the adopted estimate (unidirectional or bidirectional) are, mutually, from moderately to strongly correlated.

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Acknowledgements

The computing resources and the related technical support used for this work have been provided by CRESCO/ENEAGRID High Performance Computing infrastructure and its staff. CRESCO/ENEAGRID High Performance Computing infrastructure is funded by ENEA, the Italian National Agency for New Technologies, Energy and Sustainable Economic Development and by Italian and European research programmes (see www.cresco.enea.it for information).

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Authors and Affiliations

  1. Department of Chemistry, University of Florence, Via Lastruccia n. 3, 50019, Sesto Fiorentino, FI, Italy

    Piero Procacci

  2. ENEA, Portici Research Centre, DTE-ICT-HPC, P.le E. Fermi, 1, 80055, Portici, NA, Italy

    Guido Guarnieri

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  1. Piero Procacci
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Correspondence to Piero Procacci.

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Procacci, P., Guarnieri, G. SAMPL6 blind predictions of water-octanol partition coefficients using nonequilibrium alchemical approaches. J Comput Aided Mol Des 34, 371–384 (2020). https://doi.org/10.1007/s10822-019-00233-9

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