Substituent effects on the basicity (pK_a) of aryl guanidines and 2-(arylimino)imidazolidines: correlations of pH-metric and UV-metric values with predictions from gas-phase ab initio bond lengths

Abstract

The dissociation constants of two related series of 2-(arylimino)imidazolidine and aryl guanidine a 2 -adrenoceptor antagonists (35 compounds in total) were measured by potentiometric titrations and by UV-spectrophotometry using the 96-well microtitre plate method. The experimental values obtained using both methods were quite consistent and showed a very good agreement with the pK a values calculated using the AIBLHiCoS methodology, which uses only a single bond length obtained using ab initio calculations at a low level of theory. The prediction power of the imidazolidine and guanidine set of compounds was very good with deviations typically o0.30 and o0.24 pK a units, and a mean absolute error (MAE) of 0.23 and 0.29, respectively. The study of the quantitative effect of diverse substituents on the basicity of aryl guanidine and 2-(arylimino)imidazolidine derivatives is useful for medicinal chemists working with biologically relevant guanidine-containing molecules. † Electronic supplementary information (ESI) available: Excel sheet template for the data analysis of UV-metric pK a determination. Tables S1-S6 and Fig. S1-S4. 1 H and 13 C NMR spectra of compounds 1a and 11a-15a. See

Aqueous pKa prediction for tautomerizable compounds using equilibrium bond lengths

“…For the anti and syn conformers of the keto-enol state, an input geometry for the higher energy anti-conformation was manually generated by rotating the orientation of the O-H bond of the syn conformer by 180°. This process of generating the keto-enol anti state 15,16,[18][19][20][21][22] was repeated for the remaining 61 species.…”

“…Previous work [15][16][17][18][19][20][21][22] from our group, as well as the earlier work of others, has highlighted the utility of bond lengths [23][24][25] and Fig. 1 Structures of 1,3-diketone derivatives and schematic of our workflow.…”

A Quantitative Structure-Property Relationship Model Based on Chaos-Enhanced Accelerated Particle Swarm Optimization Algorithm and Back Propagation Artificial Neural Network

“…The QSPR model can be used to predict the activity of unknown materials and discover key influencing factors of the activity of related substances, such as groups or substituents determining the activity of the molecular structure [3,4]. Nowadays, QSPR has been applied in the fields of computer science, chemistry, materials science, medicine science, and life sciences [5,6]. swarm optimization algorithm (CAPSO) by integrating chaos theory into the improvement of the APSO algorithm.…”

A Quantitative Structure-activity Relationships Model Based on Hybrid Artificial Intelligence Methods and its Application

“…QSAR study can be used to predict the activity of unknown materials and discover key influencing factors of the activity of related substances, such as groups or substituents determining the activity of the molecular structure [3,4]. Nowadays, QSAR has been applied in the fields of computer science, chemistry, materials science, medicine science, and life sciences [5,6].…”