“…1) reveals that the molecule exists in crystal in 1H tautomeric form (N1H tautomer) in agreement with previous findings [10]. Values of bond lengths within triazole ring of MTSA are very close to other substituted thioderivatives of 1,2,4-triazole [23][24][25]. In particular, considerable differences between lengths of the N(2)-C(3) and N(1)-C(5), N(4)-C(3) and N(4)-C(5) bonds (Table 1) are observed reflecting asymmetric structure of heterocycle.…”
Molecular structure, relative stability of conformers, and tautomers of (5-methyl-1H-[1,2,4]triazol-3-ylsulfanyl)-acetic acid (MTSA) have been investigated by experimental (X-ray diffraction) and theoretical (B3LYP/ aug-cc-pVDZ) methods. It was demonstrated that in the solid state MTSA exists in N1H tautomeric form. This tautomer is not the most stable in gas phase and its stabilization is provided by environment effects.
“…1) reveals that the molecule exists in crystal in 1H tautomeric form (N1H tautomer) in agreement with previous findings [10]. Values of bond lengths within triazole ring of MTSA are very close to other substituted thioderivatives of 1,2,4-triazole [23][24][25]. In particular, considerable differences between lengths of the N(2)-C(3) and N(1)-C(5), N(4)-C(3) and N(4)-C(5) bonds (Table 1) are observed reflecting asymmetric structure of heterocycle.…”
Molecular structure, relative stability of conformers, and tautomers of (5-methyl-1H-[1,2,4]triazol-3-ylsulfanyl)-acetic acid (MTSA) have been investigated by experimental (X-ray diffraction) and theoretical (B3LYP/ aug-cc-pVDZ) methods. It was demonstrated that in the solid state MTSA exists in N1H tautomeric form. This tautomer is not the most stable in gas phase and its stabilization is provided by environment effects.
“…Mrozek and co-workers [24][25][26][27], in their study, on the HOMA of five-membered heterocyles using bond length from X-ray, also found that in the rHOMA index isoxazoles and oxazoles are not aromatic compounds and furans are classified as anti-aromatic compounds. From the results described in Table 5, it is possible to state that for six-membered heterocycles, there is a variation of aromaticity with the variation in number and position of nitrogen.…”
Section: Resultsmentioning
confidence: 95%
“…In this context, it is worth noting that HOMA is a relative index and that the maximum aromaticity is found when HOMA = 1 and this corresponds to benzene. From this, it was established that: (i) compounds with HOMA [ 0.5 are aromatic; compounds with values in the range 0.0 \ HOMA \ 0.5 are considered non-aromatic; compounds with HOMA \ 0 are considered anti-aromatic [25]. From this classification arises the first difference between HOMHED and rHOMA findings.…”
A new parametrization for the Harmonic Oscillator Model of Aromaticity (HOMA) index to determine aromaticity of heterocycles is introduced. The new HOMA for Heterocycle Electron Delocalization (HOM-HED) is based on the experimental data from electron diffraction X-ray for the reference molecules used to estimate the simple, double, and optimal bond lengths.
“…morphine (Amiphenazol, Daptazol), fungicides (Benomyl) and others. 73 These information can In summary, the selected descriptors proved that the electrostatic, hydrophobic, and H-bond donor or acceptor and aromaticity relevant descriptors and some atom-centred fragments descriptors were important structural features relevant to the binding affinity. These descriptors contained the consistent information with the results from other similar studies.…”
Section: Exploration Of the Selected Descriptorsmentioning
In this study, a novel method was developed to predict the binding affinity of protein-ligand based on a comprehensive set of structurally diverse protein-ligand complexes (PLCs). The 1300 PLCs with binding affinity (493 complexes with K(d) and 807 complexes with K(i)) from the refined dataset of PDBbind Database (release 2007) were studied in the predictive model development. In this method, each complex was described using calculated descriptors from three blocks: protein sequence, ligand structure, and binding pocket. Thereafter, the PLCs data were rationally split into representative training and test sets by full consideration of the validation of the models. The molecular descriptors relevant to the binding affinity were selected using the ReliefF method combined with least squares support vector machines (LS-SVMs) modeling method based on the training data set. Two final optimized LS-SVMs models were developed using the selected descriptors to predict the binding affinities of K(d) and K(i). The correlation coefficients (R) of training set and test set for K(d) model were 0.890 and 0.833. The corresponding correlation coefficients for the K(i) model were 0.922 and 0.742, respectively. The prediction method proposed in this work can give better generalization ability than other recently published methods and can be used as an alternative fast filter in the virtual screening of large chemical database.
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