Evaluation of log P, pKa, and log D predictions from the SAMPL7 blind challenge
Autor: | Yingying Zhang, Marilyn R. Gunner, Junjun Mao, Stefan M. Kast, Teresa Danielle Bergazin, Karol R. Francisco, Carlo Ballatore, Nicolas Tielker, David L. Mobley |
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Rok vydání: | 2021 |
Předmět: |
Octanols
Research groups Entropy Ligands SAMPL Article Prediction methods Drug Discovery Statistics Humans pKa Computer Simulation Physical and Theoretical Chemistry log P Mathematics Sulfonamides Models Statistical Computational Biology Water Computer Science Applications Models Chemical Solubility Drug Design Solvents Quantum Theory Thermodynamics Free energy calculations Software |
Zdroj: | Journal of Computer-Aided Molecular Design |
ISSN: | 1573-4951 0920-654X |
Popis: | 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. |
Databáze: | OpenAIRE |
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