In order to model the stability of lanthanide complexes with amino acids, we used a set of 20 mono-complexes of La(3+), Ce(3+), Pr(3+), Nd(3+) and Sm(3+) with glycine, alanine, valine and leucine. ...The quadratic model based on the (3)χ(v) index gave r = 0.978, S.E. = 0.08. The predictive power of the model was tested by splitting the initial set of complexes on training (N = 15) and test set (N = 5). This enabled the logarithm of the stability constant, log K(1), of the leucine complex of neodymium(III) and all four complexes of samarium(III) to be predicted with S.E. = 0.11.
Kemijski stručni jezik ne određuju samo zahtjevi struke, on se ne mijenja samo zbog razvoja kemije nego i zbog jezične politike sredine u kojoj se razvija. Prikazao sam razvoj hrvatske kemijske ...nomenklature i terminologije od 19. do 21. stoljeća, ukazujući pritom na utjecaje stranih jezika (njemačkog, češkog, engleskog). Posebnu sam pozornost poklonio raspravama unitaristički i nacionalistički (puristički) orijentiranih hrvatskih kemičara o aktualnoj jezičnoj politici prema stručnom jeziku.
This paper presents models for the estimation of stability constants (K 1 and I2 2) of nickel(II), copper(II) and zinc(II) mono- and bis-complexes with 5 Schiff bases (salicylideneglycine, ...salicylidenealanine, salicylideneserine, salicylidenephenylalanine, and salicylidenetyrosine). The models were based on the molecular-graph theory and valence molecular connectivity index of the 3rd order, 3I v , derived from it. Univariate linear models were developed for each metal separately, while in the common models for two and three metals, the indicator variable, In, was introduced. The standard error of models for the log K 1 constant was less than 0.12, while for log I2 2 models, the S.E. did not exceed 0.14.
Using molecular graph theory we studied the binding of NSFRY-NH2 and 12 related pentapeptide amides to Cu(II) as a model system for atrial natriuretic factor (ANF) peptide interactions with copper. ...Linear regression models based on the valence connectivity index of the 3rd order (3χv) reproduced experimental stability constants (log β) for 1N, 2N, 3N, and 4N coordinated complexes with the standard error of 0.30-0.39 log β units. We developed separate models for seven tyrosinic (N=28) and five non-tyrosinic peptides (N=20), and a common model for both kinds of peptides (N=48) with an indicator (dummy) variable. The results indicate additional aromatic stabilisation in 4N complexes due to metal cation-π interactions with tyrosine but not with the phenylalanine residue. We have also amended the log K and log K* values to correct miscalculations published by Janicka-Klos et al. in 2013.
The quadratic model for the prediction of stability constants of transition metal (Co2+, Ni2+, Cu2+, Zn2+, and Cd2+) complexes with four monocarboxylic amino acids (methanoic, ethanoic, propanoic, ...and butanoic) was developed. The model yielded regression coefficient r = 0.996, and standard error S.E. = 0.05 (N = 20). As a test of goodness of the model, we predicted log K1 of three Co2+ complexes (with methanoic, propanoic and butanoic acid) from the training set (N = 17), consisted of the constants of other metal complexes and log K1 of CoAc+. The model yielded predictions with the S.E. = 0.08.
Three sets of flavonoid derivatives (N=32, 40, and 74) and logarithms of their dissociation constants (log Kd) that describe flavonoid affinity toward P-glycoprotein were modelled using six ...connectivity indices. The best results were obtained with the zero-order valence molecular connectivity index (0χv) for all three sets. Standard errors of the calibration models were around 0.3, and of the constants from the test sets even a little lower, 0.22 and 0.24. Despite using only one descriptor, our model proved better in internal (cross-validation) and especially in external (test set) statistics than much more demanding methods used in previous 3D QSAR modelling.
Based on the quadratic function used previously for the estimation of copper(II) amino acid mono-complexes, we developed the model for the estimation of the stability constant K1 of cadmium(II) ...complexes with five amino acids (glycine, alanine, 2-aminobutanoic, 2-aminopentanoic, and 2-aminohexanoic acid). The model gave R2 = 0.960 and S.E. = 0.03. Also, for the first time we proposed (univariate linear) model for the estimation of K1 of complexes with monodentate ligands, namely the Cd(II) complexes with methanoic, ethanoic, propanoic, 2-methylpropanoic, butanoic, 2-methylbutanoic, 2-hydroxyethanoic, 2-hydroxypropanoic and 2-hydroxybutanoic acid. The model is capable to discriminate monodentate from bidentate ligands; much better statistic was obtained (R2 = 0.966 and S.E. = 0.05) if 2-hydroxybutanoic acid was assumed to be bidentate.
Models for estimation of the first (K1), second (K2), and overall stability constant (β2) of copper(II) chelates with naturally occurring amino acids, based on the valence connectivity index of the ...3rd order (3χv), were improved by introduction of a square term and a new graph representation for mono‐complexes (MLcor). The models gave SE=0.07, 0.05–0.07 and 0.05–0.08 for lg K1, lg K2 and lg β2 constants, respectively; models that encompass both binary and ternary bis‐complexes included indicator variable. We also validated our models on the test set which included two mono‐, two binary and two ternary Cu(II) chelates with α‐aminobutanoic acid and α‐aminopentanoic acid, not included into the calibration. The absolute differences between experimental and predicted stability constants were in the range of 0.01–0.16.
Models for estimation of the first (K1), second (K2), and overall stability constant (β2) of copper(II) chelates with naturally occurring amino acids, based on the valence connectivity index of the 3rd order (3χv), were improved by introduction of a square term and a new graph representation for mono‐complexes (MLcor). We also validated our models on the test set which included two mono‐, two binary and two ternary Cu(II) chelates with α‐aminobutanoic acid and α‐aminopentanoic acid, not included into the calibration. The absolute differences between experimental and predicted stability constants were in the range of 0.01–0.16.
Models for estimation of the first (K1), second (K2), and overall stability constant (beta2) of copper(II) chelates with naturally occurring amino acids, based on the valence connectivity index of ...the 3rd order (3χv), were improved by introduction of a square term and a new graph representation for mono-complexes (MLcor). The models gave SE=0.07, 0.05-0.07 and 0.05-0.08 for lg K1, lg K2 and lg beta2 constants, respectively; models that encompass both binary and ternary bis-complexes included indicator variable. We also validated our models on the test set which included two mono-, two binary and two ternary Cu(II) chelates with alpha-aminobutanoic acid and alpha-aminopentanoic acid, not included into the calibration. The absolute differences between experimental and predicted stability constants were in the range of 0.01-0.16.
A copper(II) complex with 1-aminocyclopropane-1-carboxylic acid assembles by apical Cu···O bonds and hydrogen-bonding interactions into discrete trimeric units that exhibit both cis and trans binding ...modes.
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