Abstract
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 ([Formula: see text]), or their logarithms (logP), 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 [Formula: see text] 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 logP 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 logP values in the range of 1.95-4.09. We describe the potentiometric logP measurement protocol used to collect this dataset using a Sirius T3, discuss the limitations of this experimental approach, and share suggestions for future logP data collection efforts for the evaluation of computational methods.
Highlights
The SAMPL (Statistical Assessment of the Modeling of Proteins and Ligands) Challenges [http://samplchallenges.github.io] are a series of blind prediction challenges for the computational chemistry community that aim to evaluate and advance computational tools for rational drug design [1]
Not all molecules selected for SAMPL6 were suitable for log P measurements using the Sirius T3, due to various reasons such as low solubility, apparent pK a value shifting out of experimental range, or log P values out of experimental range limited by the sample vial
For 13 of the selected compounds, experimental constraints set by solubility, lipophilicity, pK a properties of the analytes, and experiment analysis volume limitations of the Sirius T3 instrument resulted in an inability to achieve reliable log P measurements suitable for the blind challenge (Table S4)
Summary
The SAMPL (Statistical Assessment of the Modeling of Proteins and Ligands) Challenges [http://samplchallenges.github.io] are a series of blind prediction challenges for the computational chemistry community that aim to evaluate and advance computational tools for rational drug design [1]. These challenges focus the community on specific phenomena relevant to drug discovery—such as the contribution of force field inaccuracy to binding affinity prediction failures—and, using carefully-selected test systems, isolate these phenomena from other confounding factors.
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