期刊
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN
卷 34, 期 4, 页码 405-420出版社
SPRINGER
DOI: 10.1007/s10822-019-00271-3
关键词
Octanol-water partition coefficient; log P; Blind prediction challenge; SAMPL; Kinase inhibitor fragments; 4-Aminoquinazoline; Potentiometric log P measurement
资金
- NCI NIH HHS [P30 CA008748] Funding Source: Medline
- NIGMS NIH HHS [R01 GM108889, R01 GM124270] Funding Source: Medline
Partition coefficients describe the equilibrium partitioning of a single, defined charge state of a solute between two liquid phases in contact, typically a neutral solute. Octanol-water partition coefficients (Kowor their logarithms (log P), are frequently used as a measure of lipophilicity in drug discovery. The partition coefficient is a physicochemical property that captures the thermodynamics of relative solvation between aqueous and nonpolar phases, and therefore provides an excellent test for physics-based computational models that predict properties of pharmaceutical relevance such as protein-ligand binding affinities or hydration/solvation free energies. The SAMPL6 Part II octanol-water partition coefficient prediction challenge used a subset of kinase inhibitor fragment-like compounds from the SAMPL6 pKa prediction challenge in a blind experimental benchmark. Following experimental data collection, the partition coefficient dataset was kept blinded until all predictions were collected from participating computational chemistry groups. A total of 91 submissions were received from 27 participating research groups. This paper presents the octanol-water log P dataset for this SAMPL6 Part II partition coefficient challenge, which consisted of 11 compounds (six 4-aminoquinazolines, two benzimidazole, one pyrazolo[3,4-d]pyrimidine, one pyridine, one 2-oxoquinoline substructure containing compounds) with log P values in the range of 1.95-4.09. We describe the potentiometric log P measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future log P data collection efforts for the evaluation of computational methods.
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