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Items: 1 to 20 of 100

1.

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

Işık M, Bergazin TD, Fox T, Rizzi A, Chodera JD, Mobley DL.

J Comput Aided Mol Des. 2020 Feb 27. doi: 10.1007/s10822-020-00295-0. [Epub ahead of print]

PMID:
32107702
2.

Octanol-water partition coefficient measurements for the SAMPL6 blind prediction challenge.

Işık M, Levorse D, Mobley DL, Rhodes T, Chodera JD.

J Comput Aided Mol Des. 2019 Dec 19. doi: 10.1007/s10822-019-00271-3. [Epub ahead of print]

PMID:
31858363
3.

Prediction of the n-octanol/water partition coefficients in the SAMPL6 blind challenge from MST continuum solvation calculations.

Zamora WJ, Pinheiro S, German K, Ràfols C, Curutchet C, Luque FJ.

J Comput Aided Mol Des. 2019 Nov 27. doi: 10.1007/s10822-019-00262-4. [Epub ahead of print]

PMID:
31776809
4.

Prediction of octanol-water partition coefficients for the SAMPL6-[Formula: see text] molecules using molecular dynamics simulations with OPLS-AA, AMBER and CHARMM force fields.

Fan S, Iorga BI, Beckstein O.

J Comput Aided Mol Des. 2020 Jan 20. doi: 10.1007/s10822-019-00267-z. [Epub ahead of print]

PMID:
31960254
5.

Blind prediction of cyclohexane-water distribution coefficients from the SAMPL5 challenge.

Bannan CC, Burley KH, Chiu M, Shirts MR, Gilson MK, Mobley DL.

J Comput Aided Mol Des. 2016 Nov;30(11):927-944. doi: 10.1007/s10822-016-9954-8. Epub 2016 Sep 27.

6.

The SAMPL6 challenge on predicting octanol-water partition coefficients from EC-RISM theory.

Tielker N, Tomazic D, Eberlein L, Güssregen S, Kast SM.

J Comput Aided Mol Des. 2020 Jan 24. doi: 10.1007/s10822-020-00283-4. [Epub ahead of print]

PMID:
31981015
7.

Quantum chemical predictions of water-octanol partition coefficients applied to the SAMPL6 logP blind challenge.

Jones MR, Brooks BR.

J Comput Aided Mol Des. 2020 Jan 30. doi: 10.1007/s10822-020-00286-1. [Epub ahead of print]

PMID:
32002778
8.

Predicting octanol/water partition coefficients for the SAMPL6 challenge using the SM12, SM8, and SMD solvation models.

Ouimet JA, Paluch AS.

J Comput Aided Mol Des. 2020 Jan 30. doi: 10.1007/s10822-020-00293-2. [Epub ahead of print]

PMID:
32002781
9.

LogP prediction performance with the SMD solvation model and the M06 density functional family for SAMPL6 blind prediction challenge molecules.

Guan D, Lui R, Matthews S.

J Comput Aided Mol Des. 2020 Jan 14. doi: 10.1007/s10822-020-00278-1. [Epub ahead of print]

PMID:
31939103
10.

Measuring experimental cyclohexane-water distribution coefficients for the SAMPL5 challenge.

Rustenburg AS, Dancer J, Lin B, Feng JA, Ortwine DF, Mobley DL, Chodera JD.

J Comput Aided Mol Des. 2016 Nov;30(11):945-958. doi: 10.1007/s10822-016-9971-7. Epub 2016 Oct 7.

11.

A blind SAMPL6 challenge: insight into the octanol-water partition coefficients of drug-like molecules via a DFT approach.

Arslan E, Findik BK, Aviyente V.

J Comput Aided Mol Des. 2020 Jan 14. doi: 10.1007/s10822-020-00284-3. [Epub ahead of print]

PMID:
31939104
12.

The SAMPL5 challenge for embedded-cluster integral equation theory: solvation free energies, aqueous pK a, and cyclohexane-water log D.

Tielker N, Tomazic D, Heil J, Kloss T, Ehrhart S, Güssregen S, Schmidt KF, Kast SM.

J Comput Aided Mol Des. 2016 Nov;30(11):1035-1044. doi: 10.1007/s10822-016-9939-7. Epub 2016 Aug 23.

PMID:
27554666
13.

pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments.

Işık M, Levorse D, Rustenburg AS, Ndukwe IE, Wang H, Wang X, Reibarkh M, Martin GE, Makarov AA, Mobley DL, Rhodes T, Chodera JD.

J Comput Aided Mol Des. 2018 Oct;32(10):1117-1138. doi: 10.1007/s10822-018-0168-0. Epub 2018 Nov 7.

14.

Blind prediction of distribution in the SAMPL5 challenge with QM based protomer and pK a corrections.

Pickard FC 4th, König G, Tofoleanu F, Lee J, Simmonett AC, Shao Y, Ponder JW, Brooks BR.

J Comput Aided Mol Des. 2016 Nov;30(11):1087-1100. doi: 10.1007/s10822-016-9955-7. Epub 2016 Sep 19.

PMID:
27646286
15.

Predicting cyclohexane/water distribution coefficients for the SAMPL5 challenge using MOSCED and the SMD solvation model.

Diaz-Rodriguez S, Bozada SM, Phifer JR, Paluch AS.

J Comput Aided Mol Des. 2016 Nov;30(11):1007-1017. doi: 10.1007/s10822-016-9945-9. Epub 2016 Aug 26.

PMID:
27565796
16.

1-Octanol/Water Partition Coefficients of n-Alkanes from Molecular Simulations of Absolute Solvation Free Energies.

Garrido NM, Queimada AJ, Jorge M, Macedo EA, Economou IG.

J Chem Theory Comput. 2009 Sep 8;5(9):2436-46. doi: 10.1021/ct900214y.

17.

Use of molecular dynamics fingerprints (MDFPs) in SAMPL6 octanol-water log P blind challenge.

Wang S, Riniker S.

J Comput Aided Mol Des. 2019 Nov 19. doi: 10.1007/s10822-019-00252-6. [Epub ahead of print]

PMID:
31745704
18.

Prediction of cyclohexane-water distribution coefficients with COSMO-RS on the SAMPL5 data set.

Klamt A, Eckert F, Reinisch J, Wichmann K.

J Comput Aided Mol Des. 2016 Nov;30(11):959-967. doi: 10.1007/s10822-016-9927-y. Epub 2016 Jul 26.

PMID:
27460058
19.

SAMPL6 blind predictions of water-octanol partition coefficients using nonequilibrium alchemical approaches.

Procacci P, Guarnieri G.

J Comput Aided Mol Des. 2019 Oct 17. doi: 10.1007/s10822-019-00233-9. [Epub ahead of print]

PMID:
31624982
20.

SAMPL6 challenge results from [Formula: see text] predictions based on a general Gaussian process model.

Bannan CC, Mobley DL, Skillman AG.

J Comput Aided Mol Des. 2018 Oct;32(10):1165-1177. doi: 10.1007/s10822-018-0169-z. Epub 2018 Oct 15.

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