Open AccessAutomated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge
Author(s) -
Matthew Bahr,
Aakankschit Nandkeolyar,
John K. Kenna,
Neysa Nevins,
Luigi Da Vià,
Mehtap Işık,
John D. Chodera,
David L. Mobley
Publication year - 2021
Publication title -
journal of computer-aided molecular design
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.749
H-Index - 101
eISSN - 1573-4951
pISSN - 0920-654X
DOI - 10.1007/s10822-021-00427-0
Subject(s) - deprotonation , computer science , quantitative structure–activity relationship , drug discovery , throughput , dissociation constant , high throughput screening , biochemical engineering , biological system , data mining , chemistry , machine learning , engineering , organic chemistry , ion , telecommunications , biochemistry , wireless , biology , receptor
The goal of the Statistical Assessment of the Modeling of Proteins and Ligands (SAMPL) challenge is to improve the accuracy of current computational models to estimate free energy of binding, deprotonation, distribution and other associated physical properties that are useful for the design of new pharmaceutical products. New experimental datasets of physicochemical properties provide opportunities for prospective evaluation of computational prediction methods. Here, aqueous pK a and a range of bi-phasic logD values for a variety of pharmaceutical compounds were determined through a streamlined automated process to be utilized in the SAMPL8 physical property challenge. The goal of this paper is to provide an in-depth review of the experimental methods utilized to create a comprehensive data set for the blind prediction challenge. The significance of this work involves the use of high throughput experimentation equipment and instrumentation to produce acid dissociation constants for twenty-three drug molecules, as well as distribution coefficients for eleven of those molecules.