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

Jones, Michael R.; Brooks, Bernard R.

doi:10.1007/s10822-020-00286-1

Cited by 16 publications

(11 citation statements)

References 56 publications

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“…Previous SAMPL challenges have looked at the prediction of solvation free energies [8][9][10][11][12], guest-host [13][14][15][16][17][18][19] and protein-ligand binding affinities [20][21][22][23][24][25][26], pK a [27][28][29][30][31][32][33], distribution coefficients [34][35][36][37], and partition coefficients [38][39][40][41]. These challenges have helped uncover sources of error, pinpoint the reasons various methods performed poorly or well and their strengths and weaknesses, and facilitate (1) log P = log 10 K ow = log 10 [unionized solute] octanol [unionized solute] water dissemination of lessons learned after each challenge ends, ultimately leading to improved methods and algorithms.…”

Section: Motivation For the Log P And Pk A Challengementioning

confidence: 99%

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

Bergazin

Tielker

Zhang

et al. 2021

J Comput Aided Mol Des

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The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. The dataset was composed of a series of N-acylsulfonamides and related bioisosteres. 17 research groups participated in the log P challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water log P predictions in the SAMPL7 challenge was lower than octanol-water log P predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.

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Section: Motivation For the Log P And Pk A Challengementioning

confidence: 99%

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

Bergazin

Tielker

Zhang

et al. 2021

J Comput Aided Mol Des

View full text Add to dashboard Cite

show abstract

“…Previous SAMPL challenges have looked at the prediction of solvation free energies [8][9][10][11][12], guest-host [13][14][15][16][17][18][19] and protein-ligand binding affinities [20][21][22][23][24][25][26], pK a [27][28][29][30][31][32][33], distribution coefficients [34][35][36][37], and partition coefficients [38][39][40][41]. These challenges have helped uncover sources of error, pinpoint the reasons various methods performed poorly or well and their strengths and weaknesses, and facilitate dissemination of lessons learned after each challenge ends, ultimately leading to improved methods and algorithms.…”

Section: Motivation For the Log P And Pk A Challengementioning

confidence: 99%

Evaluation of Log P, pKa and Log D Predictions from the SAMPL7 Blind Challenge

Bergazin¹,

Tielker²,

Zhang³

et al. 2021

Preprint

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<div>The Statistical Assessment of Modeling of Proteins and Ligands (SAMPL) challenges focuses the computational modeling community on areas in need of improvement for rational drug design. The SAMPL7 physical property challenge dealt with prediction of octanol-water partition coefficients and pKa for 22 compounds. </div><div>The dataset was composed of a series of N-acylsulfonamides and related bioisosteres.</div><div>17 research groups participated in the logP challenge, submitting 33 blind submissions total. For the pKa challenge, 7 different groups participated, submitting 9 blind submissions in total. Overall, the accuracy of octanol-water logP predictions in the SAMPL7 challenge was lower than octanol-water logP predictions in SAMPL6, likely due to a more diverse dataset. Compared to the SAMPL6 pKa challenge, accuracy remains unchanged in SAMPL7.</div><div>Interestingly, here, though macroscopic pKa values were often predicted with reasonable accuracy, there was dramatically more disagreement among participants as to which microscopic transitions produced these values (with methods often disagreeing even as to the sign of the free energy change associated with certain transitions), indicating far more work needs to be done on pKa prediction methods.</div>

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“…Among those organizations, the efforts of the US NIST and the US EPA are particularly commendable because a large fraction of those databases is freely accessible, at least for single component properties. In addition to these experimental databases, there has been an increase in the number of articles reporting properties generated via DFT solutions of quantum mechanical (QM) equations, which in turn can be used to generate QSPR relationships to predict single component properties [1][2][3] . As a result of all these developments, we now have greater access to single component properties than ever before.…”

Section: Introductionmentioning

confidence: 99%

Hildebrand-Assessed Margules (HAM) interaction parameter: applications to surfactant and polar oil partition

Acosta

2022

Preprint

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The Hildebrand-Assessed Margules (HAM) method uses Henry's law constant and vapor pressure for pure components from free cheminformatic software to obtain the interaction parameter of a component "i" with water (Aiw) and its molar volume (Vmi). Inspired by the concept of the solubility parameter of Hildebrand (δi), where (Aiw/Vmi)^0.5 = (δi-δw), HAM makes a simple assessment of the binary interaction parameter Aij: (δi-δj) =(Aij/Vmi)^0.5 = (δi-δw)-(δi-δw)= (Aiw/Vmi)^0.5- (Ajw/Vmi)^0.5. The Aij predictions from this relatively simple expression were compared to literature Aij data obtained from activity coefficients of "i" at infinite dilution in a solvent "j". The performance of the HAM framework was compared to other predictive models, UNIFAC, MOSCED, COSMO-RS, HSP and the original Hildebrand model. The HAM method overpredicted Aij (>1Aij unit) in systems where the solvent was an acid or a base that could dissociate in the presence of water. However, for most systems (small polar molecules, short chain alcohols, medium chain alcohols, aromatics and alkanes), the Aij values were predicted within 1 Aij unit and commonly within 0.5 Aij units. A systemic underprediction of Aij was observed when the HAM-predicted log P was compared to predictions from the ACD/Labs software, which required the introduction of a correction term. The corrected HAM method reproduced the partition coefficient of surfactant and polar molecules with an RMSE of 1.05 and an NRMSE of 15%, comparable to other models that require more inputs and resources.

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

Cited by 16 publications

References 56 publications

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge

Evaluation of Log P, pKa and Log D Predictions from the SAMPL7 Blind Challenge

Hildebrand-Assessed Margules (HAM) interaction parameter: applications to surfactant and polar oil partition

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