For ranked submissions, we identified a single consistently well-performing method that was ranked in the top three according to RMSE, MAE, Kendall's Tau, and R 2 (all statistics are available in Table ). The best-performing method was TFE IEFPCM MST + IEFPCM/MST, which used a QM-based physical method for pK a and log P predictions . The IEFPCM/MST model has previously been used to predict the log D of over 35 ionizable drugs, where it achieved a RMSE of 1.6 , all little worse than a RMSE of 1.3 in SAMPL7. The pK a prediction protocol used in the challenge is described in Section 3.2.2, where it was ranked among the consistently well performing pK a methods. Some SEM values are too small to be seen under the data points. Performance statistics of all methods is available in Table . lo g P _ e x p e r im e n t a l + E C _ R I S M lo g P _ e x p e r im e n t a l + p K a _ e x p e r im e n t a l 1A, except it includes some additional pK a and log P combinations (for log D estimation). Method logP_experimental + EC_RISM combines the experimental log P with the top performing pK a method (based on RMSE). Method logP_experimental + pKa_experimental combines the experimental log P and pK a value. Method TFE MLR + EC_RISM combines the best performing (based on RMSE) log P and pK a methods. Method TFE MLR + pKa_experimental combines the best performing (based on RMSE) log P method with the experimental pK a . Method logP_experimental + DFT_M05-2X_SMD combines the experimental log P with an average performing pK a method. Method NES-1 (GAFF2/OPC3) B + pKa_experimental combines a log P method with average performance with the experimental pK a . All other methods are the same as in Figure .3.1A.