pKa measurements for the SAMPL6 prediction challenge for a set of kinase inhibitor-like fragments

Abstract

Determining the net charge and protonation states populated by a small molecule in an environment of interest or the cost of altering those protonation states upon transferto another environment is a prerequisite for predicting its physicochemical and pharmaceutical properties. The environment of interest can be aqueous, an organic solvent, a protein binding site, or a lipid bilayer. Predicting the protonation state of a small molecule is essential to predicting its interactions with biological macromolecules using computational models. Incorrectly modeling the dominant protonation state, shifts in dominant protonation state, or the population of significant mixtures of protonation states can lead to large modeling errors that degrade the accuracy of physical modeling. Low accuracy hinders the use of physical modeling approaches for molecular design. For small molecules, the acid dissociation constant (pKa) is the primary quantity needed to determine the ionic states populated by a molecule in an aqueous solution at a given pH. As a part of SAMPL6 community challenge, we organized a blind pKa prediction component to assess the accuracy with which contemporary pKa prediction methods can predict this quantity, with the ultimate aim of assessing the expected impact on modeling errors this would induce. While a multitude of approaches for predicting pKa values currently exist, predicting the pKas of drug-like molecules can be difficult due to challenging properties such as multiple titratable sites, heterocycles, and tautomerization. For this challenge, we focused on set of 24 small molecules selected to resemble selective kinase inhibitors—an important class of therapeutics replete with titratable moieties. Using a Sirius T3 instrument that performs automated acid- base titrations, we used UV absorbance-based pKa measurements to construct a high-quality experimental reference dataset of macroscopic pKas for the evaluation of computational pKa prediction methodologies that was utilized in the SAMPL6 pKa challenge. For several compounds in which the microscopic protonation states associated with macroscopic pKas were ambiguous, we performed follow-up NMR experiments to disambiguate the microstates involved in the transition. This dataset provides a useful standard benchmark dataset for the evaluation of pKa prediction methodologies on kinase inhibitor-like compounds.

Overview of the SAMPL6 pKaChallenge: Evaluating small molecule microscopic and macroscopic pKapredictions

“…Heterocycles that are frequently found in FDA-approved kinase inhibitors were represented in this set. The compound selection process was described in depth in the prior publication reporting SAMPL6 p K a Challenge experimental data collection [8]. The distribution of molecular weights, experimental p K a values, number of rotatable bonds, and heteroatom to carbon ratio are depicted in Fig.…”

“… A Histogram of spectrophotometric p K a measurements collected with Sirius T3 [8]. The overlaid rug plot indicates the actual values.…”

“…Different experimental methods are sensitive to changes in the total charge or the location of individual protons, so they measure different definitions of p K a s, as explained in more detail in prior work [8]. Most common p K a measurement techniques such as potentiometric and spectrophotometric methods measure macroscopic p K a s, while NMR measurements can determine microscopic p K a s by measuring microstate populations with respect to pH.…”

“…The calculations were improved by including free energy penalty of the neutral state which relies on obtaining an accurate p K a prediction [11]. With the goal of deconvolutingthe different sources of error contributing to the large errors observed in the SAMPL5 log D Challenge, we organized separate p K a and log P challenges in SAMPL6 [8,13,14]. For this iteration of the SAMPL challenge, we isolated the problem of predicting aqueous protonation states and associated p K a values.…”

The SAMPL6 challenge on predicting octanol–water partition coefficients from EC-RISM theory

“…The prediction of physicochemical properties of small, drug-like molecules has been the focus of the Statistical Assessment of the Modeling of Proteins and Ligands series of challenges for several years [1]. In the latest instance, a subset of the molecules provided during the SAMPL6 challenge for the prediction of acidity constants (pK a ) [2,3] was selected by the organizers to challenge the community again with the task to predict their neutral-state partitioning thermodynamics measured by the octanol-water partition coefficients, log P [4]. Compared to the pK a prediction challenge the resulting tasks partly overlap (solvation properties in an aqueous phase, adequate treatment of tautomeric or "microstates"), but the problem of partition coefficients, translated into the difference of solvation Gibbs (free) energies, implies additional problems.…”