Maceuticals. JC, CP, and IM are personnel of Bayer HealthCare Pharmaceuticals. CP owns stock in Bayer AG. CK is an employee of Bayer Pharma AG. MJS has received consultancy costs and analysis assistance from Bayer HealthCare Pharmaceuticals and Eisai; consultancy fees/honorarium and study assistance from AstraZeneca and GenzymeSanofi; consultancy charges from Exelixis; and consultancy fees/honorarium from Sobi.Author Manuscript Author Manuscript Author Manuscript Author Manuscript
SvobodovVaekovet al. Journal of Cheminformatics 2013, 5:18 a r a http://www.jcheminf.com/content/5/RESEARCH ARTICLEOpen AccessPredicting pKa values from EEM atomic chargesRadka SvobodovVaekov1 , Stanislav Geidl1 , CrinaMaria Ionescu1 , Ondej Skehota1 , a r a r r c Toms Bouchal1 , David Sehnal1 , Ruben Abagyan2 and Jaroslav Ko a1 aAbstract The acid dissociation continual pKa is really a essential molecular home, and there’s a sturdy interest within the improvement of dependable and speedy procedures for pKa prediction. We’ve got evaluated the pKa prediction capabilities of QSPR models based on empirical atomic charges calculated by the Electronegativity Equalization Approach (EEM). Specifically, we collected 18 EEM parameter sets designed for eight distinctive quantum mechanical (QM) charge calculation schemes. Afterwards, we prepared a education set of 74 substituted phenols. Also, for every single molecule we generated its dissociated kind by removing the phenolic hydrogen. For each of the molecules in the instruction set, we then calculated EEM charges working with the 18 parameter sets, and also the QM charges making use of the 8 above talked about charge calculation schemes. For every single style of QM and EEM charges, we developed one particular QSPR model employing charges in the nondissociated molecules (3 descriptor QSPR models), and one particular QSPR model primarily based on charges from both dissociated and nondissociated molecules (QSPR models with five descriptors).(R)-JQ-1 (carboxylic acid) In stock Afterwards, we calculated the top quality criteria and evaluated all the QSPR models obtained.2′-Deoxyadenosine custom synthesis We discovered that QSPR models employing the EEM charges proved as a superb strategy for the prediction of pKa (63 of these models had R2 0.PMID:33565366 9, while the most beneficial had R2 = 0.924). As expected, QM QSPR models offered far more correct pKa predictions than the EEM QSPR models however the differences weren’t significant. In addition, a major advantage with the EEM QSPR models is that their descriptors (i.e., EEM atomic charges) can be calculated markedly more quickly than the QM charge descriptors. Moreover, we identified that the EEM QSPR models will not be so strongly influenced by the selection of the charge calculation approach because the QM QSPR models. The robustness of the EEM QSPR models was subsequently confirmed by crossvalidation. The applicability of EEM QSPR models for other chemical classes was illustrated by a case study focused on carboxylic acids. In summary, EEM QSPR models constitute a rapidly and correct pKa prediction approach that can be utilized in virtual screening.Keywords and phrases: Dissociation continuous, Quantitative structureproperty connection, QSPR, Partial atomic charges, Electronegativity equalization system, EEM, Quantum mechanics, QMBackgroundThe acid dissociation continual pKa is an important molecular home, and its values are of interest in pharmaceutical, chemical, biological and environmental research. The pKa values have found application in numerous places, like the evaluation and optimization of candidate drug molecules [13], ADME profiling [4,5], pharmacokinetics [6], understanding of proteinligand in.
