Assessing the accuracy of octanol-water partition coefficient predictions in the SAMPL6 Part II log P Challenge

Mehtap Işık, Teresa Danielle Bergazin, Thomas Fox,  Andrea Rizzi, John D. Chodera, and David L. Mobley.
Journal of Computer Aided Molecular Design, 34:335, 2020. [DOI] [PDF] [bioRxiv] [GitHub]

We report the performance assessment of the 91 methods that were submitted to the SAMPL6 blind challenge for predicting octanol-water partition coefficient (logP) measurements. The average RMSE of the most accurate five MM-based, QM-based, empirical, and mixed approach methods based on RMSE were 0.92±0.13, 0.48±0.06, 0.47±0.05, and 0.50±0.06, respectively.

Octanol-water partition coefficient measurements for the SAMPL6 Blind Prediction Challenge

Mehtap Işık, Dorothy Levorse, David L. Mobley, Timothy Rhodes, and John D. Chodera.
Journal of Computer Aided Molecular Design 34:405, 2020. [DOI] [bioRxiv] [data] [GitHub]

We describe the design and data collection (and associated challenges) for the SAMPL6 part II logP octanol-water blind prediction challenge, where the goal was to benchmark the accuracy of force fields for druglike molecules (here, molecules resembling kinase inhibitors).