Multiconformation, Density Functional Theory-Based pK_a Prediction in Application to Large, Flexible Organic Molecules with Diverse Functional Groups

Abstract

We consider the conformational flexibility of molecules and its implications for micro- and macro-pK. The corresponding formulas are derived and discussed against the background of a comprehensive scientific and algorithmic description of the latest version of our computer program Jaguar pK, a density functional theory-based pK predictor, which is now capable of acting on multiple conformations explicitly. Jaguar pK is essentially a complex computational workflow incorporating research and technologies from the fields of cheminformatics, molecular mechanics, quantum mechanics, and implicit solvation models. The workflow also makes use of automatically applied empirical corrections which account for the systematic errors resulting from the neglect of explicit solvent interactions in the algorithm's implicit solvent model. Applications of our program to large, flexible organic molecules representing several classes of functional groups are shown, with a particular emphasis in illustrations laid on drug-like molecules. It is demonstrated that a combination of aggressive conformational search and an explicit consideration of multiple conformations nearly eliminates the dependence of results on the initially chosen conformation. In certain cases this leads to unprecedented accuracy, which is sufficient for distinguishing stereoisomers that have slightly different pK values. An application of Jaguar pK to proton sponges, the pK of which are strongly influenced by steric effects, showcases the advantages that pK predictors based on quantum mechanical calculations have over similar empirical programs.

Chemical Transformations Approaching Chemical Accuracy via Correlated Sampling in Auxiliary-Field Quantum Monte Carlo

“…CO 2 and O 2 reduction). [45,46] A reliable ab initio method to calculate deprotonation free energies would provide an improvement upon existing computational approaches to determining pKa's and protonation states, [47][48][49] which would have significant ramifications for drug discovery, [50,51] materials science, [52][53][54] and the structural determination of biological complexes. [55,56] In the context of chemical catalysis, proton removal is known to be the rate-limiting step in many important reactions, e.g.…”

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“…A high degree of prediction accuracy in modern terms is considered to be within 1 pK a unit per prediction, with a mean absolute error (MAE) for a test set of less than 0.5 units. 35 As the Table 1 pK a values of aryl/pyridinyl guanidines and iminoimidazolidines calculated using AIBLHiCos and determined experimentally by potentiometric and spectrophotometric methods…”

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“…This is again an approximation as previous work by Bochevarov et.al. [11] have shown that multiple low lying conformations do contribute to the deprotonation free energy. There can be a couple of different strategies to handle this phenomenon.…”

“…Machine learning models for specific functional groups are trained based on these descriptors [10]. Notably, these methods ignore the three dimensional conformation of the compound explicitly [11]. Although training the models might be expensive in terms of curating experimental pKa data for generating appropriate models, subsequent pKa prediction using trained models can be very fast and inexpensive.…”