Predictions:
SAMPL9-1,-0.43,0.01,1.17
SAMPL9-2,-4.03,0.01,1.17
SAMPL9-3,-6.65,0.01,1.17
SAMPL9-4,-3.46,0.01,1.17
SAMPL9-5,-5.64,0.01,1.17
SAMPL9-6,6.88,0.01,1.17
SAMPL9-7,-6.76,0.01,1.17
SAMPL9-8,-2.77,0.01,1.17
SAMPL9-9,-6.30,0.01,1.17
SAMPL9-10,-0.35,0.01,1.17
SAMPL9-11,8.28,0.01,1.17
SAMPL9-12,6.59,0.01,1.17
SAMPL9-13,1.18,0.01,1.17
SAMPL9-14,0.51,0.01,1.17
SAMPL9-15,7.77,0.01,1.17
SAMPL9-16,-2.39,0.01,1.17


Participant name:
Stefan M. Kast, Michael Strobl, Stefan Guessregen, Nicolas Tielker

Participant organization:
TU Dortmund University

Name:
EC-RISM_TFE_P1

Compute time:
45.3

Computing and hardware:
All calculations were conducted on the LiDO 3 high performance cluster of TU Dortmund University. Calculations were automatically scheduled and ran on either an Intel Xeon E5-4604v4 or an Intel Xeon E5-2640v4 CPU, depending on node availability.

Software:
Schrödinger Suite 2022-2
Gaussian 16 Rev C.01
3D RISM (inhouse development)
EC-RISM (inhouse development)
Python 3.6
Anaconda2018.12
Amber 12
Mathematica 11.3 (Wolfram)

Category:
Physical (QM)

Method:
Geometries were generated using Schrödinger Suite 2022-2 with the OPLS4/Water and OPLS4/CHCl3 (to approximate toluene) force fields with mixed torsional/low-mode sampling, 100 steps per RB and 1000 steps max (default parameters), energy window 21.0 and RMSD cutoff of 1.5. These conformers were then optimized using Gaussian 16 Rev C.01 with IEF-PCM using default settings for water and toluene, respectively, at the B3LYP/6-311+G(d,p) level of theory and clustered to remove duplicate conformations.
For each microstate only up to 5 conformations with the lowest PCM energies for each solvent were treated with EC-RISM//MP2/6-311+G(d,p) using the PSE2 closure for water and the PSE1 closure for toluene [REF1] and the resulting EC-RISM energies corrected using the general formula (c_1*mu_{ex}+c_2*PMV_{EC-RISM}+c_3), with the correction parameters trained using the Minnesota Solvation Database (MNSOL). For water not all parameters were used, i.e. c_1 = 1 and c_3 = 0, because the additional parameters were found to be of no predictive value in previous challenges [REF2]. For toluene the same approach was used due to good performance on a small, independent set of toluene-water partition coefficents.
The transfer free energy of a compound was then calculated by dG_trans=G_{tol}-G_{wat}, where G_{m} refers to the partition function estimate of the solvent specific free energy by summing over the conformational and tautomer states [REF3]. 
The SEM was estimated as the convergence criterion for a single EC-RISM calculation. The uncertainty was estimated as the RMSE of a number of MNSOL compounds for which water-toluene TFEs could be found in the literature [REF4], with G_{wat} and G_{tol} taken from the training set data.

References
[REF1] N. Tielker, D. Tomazic, J. Heil, T. Kloss, S. Ehrhart, S. Guessregen, K. F. Schmidt, S. M. Kast, J. Comput.-Aided Mol. Des. 30, 1035-1044 (2016).
[REF2] N. Tielker, L. Eberlein, S. Guessregen, S. M. Kast, J. Comput.-Aided Mol. Des. 32, 1151-1163 (2018).
[REF3] N. Tielker, D. Tomazic, L. Eberlein, S. Guessregen, S. M. Kast, J. Comput.-Aided Mol. Des. 34, 453-461 (2020).
[REF4] A. M. Zissimos, M. H. Abraham, M. C. Barker, K. J. Box, K, Y. Tam, J. Chem. Soc., Perkin Trans. 2, 470-477 (2002).

Additional microstates
Name,SMILES,population in water,population in toluene
SAMPL9-1_micro000,CCCSc1ccc2c(c1)[nH]c(n2)NC(=O)OC,62.47%,59.06%
SAMPL9-1_extra001,CCCSc1ccc2c(c1)nc([nH]2)NC(=O)OC,37.20%,40.94%
SAMPL9-1_extra002,CCCSc1ccc2c(c1)[nH]/c(=N\C(=O)OC)/[nH]2,0.33%,0.00%
SAMPL9-6_micro000,CNC[C@H](O)c1ccc(O)c(O)c1,100.00%,100.00%
SAMPL9-6_extra001,CNC[C@H](O)C1=C[C@@H](C(=O)C=C1)O,0.00%,0.00%
SAMPL9-10_micro000,C[C@@H](C(=O)O)c1cccc(c1)C(=O)c1ccccc1,100.00%,100.00%
SAMPL9-10_extra001,CC(=C(O)O)c1cccc(c1)C(=O)c1ccccc1,0.00%,0.00%
SAMPL9-11_micro000,CCn1cc(C(=O)O)c(=O)c2ccc(C)nc12,100.00%,100.00%
SAMPL9-11_extra001,CCn1cc(C(=O)[O-])c(=O)c2ccc(C)[nH+]c12,0.00%,0.00%
SAMPL9-12_micro000,CC(=O)Nc1ccc(O)cc1,100.00%,100.00%
SAMPL9-12_extra001,C=C(O)Nc1ccc(O)cc1,0.00%,0.00%
SAMPL9-12_extra002,C=C(O)NC1=CCC(=O)C=C1,0.00%,0.00%
SAMPL9-12_extra003,CC(=O)NC1=CCC(=O)C=C1,0.00%,0.00%
SAMPL9-15_micro000,Cc1cc(C)nc(NS(=O)(=O)c2ccc(N)cc2)n1,94.64%,100.00%
SAMPL9-15_extra001,Cc1cc(C)[nH]/c(=N/S(=O)(=O)c2ccc(N)cc2)/n1,5.36%,0.00%

Ranked:
False