# WATER-TOLUENE (ΔG_toluene - ΔG_water) TRANSFER FREE ENERGY PREDICTIONS
#
# This file will be automatically parsed. It must contain the following four elements:
# predictions, name of method, software listing, and method description.
# These elements must be provided in the order shown with their respective headers.
#
# Any line that begins with a # is considered a comment and will be ignored when parsing.
#
# 
# PREDICTION SECTION
#
# It is mandatory to submit water to toluene (ΔG_toluene - ΔG_water) transfer free energy (TFE) predictions for all 16 molecules.
# Incomplete submissions will not be accepted.
# The energy units must be in kcal/mol.

# Please report the general molecule `ID tag` in the form of `SAMPL9-XX` (e.g. SAMPL9-1, SAMPL9-2, etc).
# Please report TFE standard error of the mean (SEM) and TFE model uncertainty.
#
# The data in each prediction line should be structured as follows:
# ID tag, TFE, TFE SEM, TFE model uncertainty
#
# If you use a microstate other than the challenge provided microstate, please note SMILES strings of microstates you used in your submission, such as in the methods section.
#
# The list of predictions must begin with the 'Predictions:' keyword as illustrated here.
Predictions:
SAMPL9-1,-8.71,0.06,1.50
SAMPL9-2,-6.62,0.09,1.50
SAMPL9-3,-8.87,0.04,1.50
SAMPL9-4,-10.50,0.05,1.50
SAMPL9-5,-8.23,0.09,1.50
SAMPL9-6,1.72,0.05,1.50
SAMPL9-7,-7.09,0.15,1.50
SAMPL9-8,-5.99,0.14,1.50
SAMPL9-9,-8.17,0.04,1.50
SAMPL9-10,-7.22,0.04,1.50
SAMPL9-11,-4.54,0.03,1.50
SAMPL9-12,0.36,0.03,1.50
SAMPL9-13,-4.56,0.17,1.50
SAMPL9-14,-6.92,0.04,1.50
SAMPL9-15,2.59,0.04,1.50
SAMPL9-16,-8.45,0.07,1.50

#
#
# Please list your name, using only UTF-8 characters as described above. The "Participant name:" entry is required.
Participant name:
Oliver Beckstein/Bogdan I. Iorga

#
#
# Please list your organization/affiliation, using only UTF-8 characters as described above.
Participant organization:
Arizona State University, USA/ICSN, CNRS, Gif-sur-Yvette, France

#
#
# NAME SECTION
#
# Please provide an informal but informative name of the method used.
# The name must not exceed 40 characters.
# The 'Name:' keyword is required as shown here.
Name:
# SAMPL9_logP_MDPOW_OPLS-AA_TIP4P
MD (OPLS-AA/TIP4P)

#
#
# COMPUTE TIME SECTION
#
# Please provide the average compute time across all of the molecules.
# For physical methods, report the GPU and/or CPU compute time in hours.
# For empirical methods, report the query time in hours.
# Create a new line for each processor type.
# The 'Compute time:' keyword is required as shown here.
Compute time:
11076 hours, CPU

#
# COMPUTING AND HARDWARE SECTION
#
# Please provide details of the computing resources that were used to train models and make predictions.
# Please specify compute time for training models and querying separately for empirical prediction methods.
# Provide a detailed description of the hardware used to run the simulations.
# The 'Computing and hardware:' keyword is required as shown here.
Computing and hardware:
All the simulations were performed in parallel (8 cores for each simulation) on cluster nodes running with CentOS6 and 4 CPU Intel Xeon E5-4627 v3 @ 2.60GHz.

# SOFTWARE SECTION
#
# List all major software packages used and their versions.
# Create a new line for each software.
# The 'Software:' keyword is required.
Software:
Gromacs 2020.3
MDPOW 0.8.0-dev
mol2ff

# METHOD CATEGORY SECTION
#
# State which method category your prediction method is better described as:
# `Physical (MM)`, `Physical (QM)`, `Empirical`, or `Mixed`.
# Pick only one category label.
# The `Category:` keyword is required.
Category:
physical (MM)

# METHOD DESCRIPTION SECTION
#
# Methodology and computational details.
# Level of details should be roughly equivalent to that used in a publication.
# Please include the values of key parameters with units.
# Please explain how statistical uncertainties were estimated.
#
# If you have evaluated additional microstates, please report their SMILES strings and populations of all the microstates in this section.
# If you used a microstate other than the challenge provided microstate (`SMXX_micro000`), please list your chosen `Molecule ID` (in the form of `SMXX_extra001`) along with the SMILES string in your methods description.
#
# Use as many lines of text as you need.
# All text following the 'Method:' keyword will be regarded as part of your free text methods description.
Method:
Alchemical free energy calculations were performed in explicit
solvent, following the protocol described in [1-3].  Parameters were generated with the
OPLS-AA mol2ff software (B.I. Iorga, unpublished; also used in [1-3]) for
OPLS-AA with the TIP4P water model. Files were prepared for Gromacs
2020.3. Autocorrelation analysis and the multistate Bennett acceptance ratio (MBAR)
were performed with the alchemlyb Python package (https://github.com/alchemistry/alchemlyb),
release 1.0.1 as integrated into MDPOW. Errors are reported as errors of the mean (see [1-3]). The
model uncertainty was estimated on the basis of the results from [2].
[1] Kenney, I. M., Beckstein, O., and Iorga, B. I. (2016) Prediction of cyclohexane-water
distribution coefficients for the SAMPL5 data set using molecular dynamics simulations with
the OPLS-AA force field, J. Comput. Aided Mol. Des. 30(11):1045-1058 DOI: 10.1007/s10822-016-9949-5.
[2] Fan, S., Iorga, B. I., and Beckstein, O. (2020) Prediction of octanol-water partition coefficients for
the SAMPL6-logP molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields,
J Comput Aided Mol Des 34(5):543-560 DOI: 10.1007/s10822-019-00267-z.
[3] Fan S, Nedev H, Vijayan R, Iorga BI, Beckstein O. (2021) Precise force-field-based
calculations of octanol-water partition coefficients for the SAMPL7 molecules. J
Comput Aided Mol Des. 35(7):853-870 DOI: 10.1007/s10822-021-00407-4.

#
#
# All submissions must either be ranked or non-ranked.
# Only one ranked submission per participant is allowed.
# Multiple ranked submissions from the same participant will not be judged.
# Non-ranked submissions are accepted so we can verify that they were made before the deadline.
# The "Ranked:" keyword is required, and expects a Boolean value (True/False)
Ranked:
False