SAMPL4

The SAMPL4 challenge was held in 2013 and culminated in a September workshop, which involved about 30 participants and was held at Stanford University. SAMPL4 provided three types of computational challenge:

• Prediction of protein-ligand binding, for HIV integrase with nearly 400 frament-like compounds.
• Prediction of aqueous binding free energies of small guest molecules to two model receptors, also known as hosts: pumpkin-shaped cucurbit[7]uril, and the basket-shaped octa-acid host.
• Prediction of hydration free energies for about 50 small molecules.

Many of the papers from this exercise were published in a special issue (volume 28 issue 3) of the Journal of Computer-Aided Molecular Design:

1. Sandberg L. Predicting hydration free energies with chemical accuracy: the SAMPL4 challenge. J Comput Aided Mol Des. 2014;28: 211–219. doi:10.1007/s10822-014-9725-3
2. Reinisch J, Klamt A. Prediction of free energies of hydration with COSMO-RS on the SAMPL4 data set. J Comput Aided Mol Des. 2014;28: 169–173. doi:10.1007/s10822-013-9701-3
3. Park H. Extended solvent-contact model approach to SAMPL4 blind prediction challenge for hydration free energies. J Comput Aided Mol Des. 2014;28: 175–186. doi:10.1007/s10822-014-9729-z
4. Muddana HS, Sapra NV, Fenley AT, Gilson MK. The SAMPL4 hydration challenge: evaluation of partial charge sets with explicit-water molecular dynamics simulations. J Comput Aided Mol Des. 2014;28: 277–287. doi:10.1007/s10822-014-9714-6
5. Mobley DL, Wymer KL, Lim NM, Guthrie JP. Blind prediction of solvation free energies from the SAMPL4 challenge. J Comput Aided Mol Des. 2014;28: 135–150. doi:10.1007/s10822-014-9718-2
6. Manzoni F, Söderhjelm P. Prediction of hydration free energies for the SAMPL4 data set with the AMOEBA polarizable force field. J Comput Aided Mol Des. 2014;28: 235–244. doi:10.1007/s10822-014-9733-3
7. Li L, Dill KA, Fennell CJ. Testing the semi-explicit assembly model of aqueous solvation in the SAMPL4 challenge. J Comput Aided Mol Des. 2014;28: 259–264. doi:10.1007/s10822-014-9712-8
8. Koziara KB, Stroet M, Malde AK, Mark AE. Testing and validation of the Automated Topology Builder (ATB) version 2.0: prediction of hydration free enthalpies. J Comput Aided Mol Des. 2014;28: 221–233. doi:10.1007/s10822-014-9713-7
9. König G, Iv FCP, Mei Y, Brooks BR. Predicting hydration free energies with a hybrid QM/MM approach: an evaluation of implicit and explicit solvation models in SAMPL4. J Comput Aided Mol Des. 2014;28: 245–257. doi:10.1007/s10822-014-9708-4
10. Guthrie JP. SAMPL4, a blind challenge for computational solvation free energies: the compounds considered. J Comput Aided Mol Des. 2014;28: 151–168. doi:10.1007/s10822-014-9738-y
11. Genheden S, Martinez AIC, Criddle MP, Essex JW. Extensive all-atom Monte Carlo sampling and QM/MM corrections in the SAMPL4 hydration free energy challenge. J Comput Aided Mol Des. 2014;28: 187–200. doi:10.1007/s10822-014-9717-3
12. Fu J, Liu Y, Wu J. Fast prediction of hydration free energies for SAMPL4 blind test from a classical density functional theory. J Comput Aided Mol Des. 2014;28: 299–304. doi:10.1007/s10822-014-9730-6
13. Ellingson BA, Geballe MT, Wlodek S, Bayly CI, Skillman AG, Nicholls A. Efficient calculation of SAMPL4 hydration free energies using OMEGA, SZYBKI, QUACPAC, and Zap TK. J Comput Aided Mol Des. 2014;28: 289–298. doi:10.1007/s10822-014-9720-8
14. Coleman RG, Sterling T, Weiss DR. SAMPL4 & DOCK3.7: lessons for automated docking procedures. J Comput Aided Mol Des. 2014;28: 201–209. doi:10.1007/s10822-014-9722-6
15. Beckstein O, Fourrier A, Iorga BI. Prediction of hydration free energies for the SAMPL4 diverse set of compounds using molecular dynamics simulations with the OPLS-AA force field. J Comput Aided Mol Des. 2014;28: 265–276. doi:10.1007/s10822-014-9727-1