In silico prediction of ionization constants of drugs.
Most pharmacologically active molecules contain one or more ionizing groups, and it is well-known that knowledge of the ionization state of a drug, indicated by the pKa value, is critical for understanding many properties important to the drug discovery and development process. The ionization state of a compound directly influences such important pharmaceutical characteristics as aqueous solubility, permeability, crystal structure, etc. Tremendous advances have been made in the field of experimental determination of pKa, in terms of both quantity/speed and quality/accuracy. However, there still remains a need for accurate in silico predictions of pKa both to estimate this parameter for virtual compounds and to focus screening efforts of real compounds. The computer program SPARC (SPARC Performs Automated Reasoning in Chemistry) was used to predict the ionization state of a drug. This program has been developed based on the solid physical chemistry of reactivity models and applied to successfully predict numerous physical properties as well as chemical reactivity parameters. SPARC predicts both macroscopic and microscopic pKa values strictly from molecular structure. In this paper, we describe the details of the SPARC reactivity computational methods and its performance on predicting the pKa values of known drugs as well as Pfizer internal discovery/development compounds. A high correlation (r2=0.92) between experimental and the SPARC calculated pKa values was obtained with root-mean-square error (RMSE) of 0.78 log unit for a set of 123 compounds including many known drugs. For a set of 537 compounds from the Pfizer internal dataset, correlation coefficient r2=0.80 and RMSE=1.05 were obtained.
Lee
Pil H
PH
Computer Assisted Drug Discovery, Research Formulation, Pharmaceutical Science, Chemical Technology, Lead Discovery Group, Pfizer Global Research and Development, 2800 Plymouth Road, Ann Arbor, Michigan 48105, USA. pilhlee@gmail.com
Ayyampalayam
Saravanaraj N
SN
Carreira
Lionel A
LA
Shalaeva
Marina
M
Bhattachar
Shobha
S
Coselmon
Rachel
R
Poole
Salwa
S
Gifford
Eric
E
Lombardo
Franco
F
D003198:Computer Simulation; D019075:Electrophoresis, Capillary; D006860:Hydrogen Bonding; D007477:Ions / Q000737:chemistry*; D008956:Models, Chemical; D015394:Molecular Structure; D004364:Pharmaceutical Preparations / Q000737:chemistry*; D012984:Software; D055672:Static Electricity; D013237:Stereoisomerism; D013816:Thermodynamics
D007477:Ions; D004364:Pharmaceutical Preparations