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Journal article
Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge
JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, v 35(11), pp 1141-1155
Nov 2021
PMID: 34714468
Abstract
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.
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Details
- Title
- Automated high throughput pKa and distribution coefficient measurements of pharmaceutical compounds for the SAMPL8 blind prediction challenge
- Publication Details
- JOURNAL OF COMPUTER-AIDED MOLECULAR DESIGN, v 35(11), pp 1141-1155
- Publisher
- SPRINGER; DORDRECHT
- Number of pages
- 14
- Grant note
- We appreciate the National Institutes of Health for its support of the SAMPL project via R01GM124270 to David L. Mobley (UC Irvine). The authors would also like to acknowledge Lisa McQueen and Alan Graves (formerly of GSK) for their contributions during the early stages of establishing the partnership, collecting the materials necessary for the study, and providing chemometrics insight. Lastly, we acknowledge the guidance and support from Kenneth Wells of GSK.
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Drexel University
- Web of Science ID
- WOS:000712731400001
- Scopus ID
- 2-s2.0-85118379258
- Other Identifier
- 991021860755904721
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- Collaboration types
- Industry collaboration
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Biochemistry & Molecular Biology
- Biophysics
- Computer Science, Interdisciplinary Applications