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

ABSTRACT: 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 pK_a 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 pK_a 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 pK_a challenge, accuracy remains unchanged in SAMPL7. Interestingly, here, though macroscopic pK_a 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 pK_a prediction methods.

SUBMITTER: Bergazin TD

PROVIDER: S-EPMC8224998 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Evaluation of log P, pK<sub>a</sub>, and log D predictions from the SAMPL7 blind challenge.

Bergazin Teresa Danielle TD Tielker Nicolas N Zhang Yingying Y Mao Junjun J Gunner M R MR Francisco Karol K Ballatore Carlo C Kast Stefan M SM Mobley David L DL

Journal of computer-aided molecular design 20210624 7

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 pK<sub>a</sub> 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 tota ...[more]

PMID: 34169394

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Dataset Information

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

Publications

Evaluation of log P, pK<sub>a</sub>, and log D predictions from the SAMPL7 blind challenge.

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Dataset Information

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

Publications

Evaluation of log P, pK<sub>a</sub>, and log D predictions from the SAMPL7 blind challenge.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

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