Foreword

MATH/CHEM/COMP 2000 (MCC 2000), the Fifteenth Dubrovnik International Course & Conference on the Interfaces between Mathematics, Chemistry and Computer Science

Foreword

MATH/CHEM/COMP 2000 (MCC 2000), the Fifteenth Dubrovnik International Course & Conference on the Interfaces between Mathematics, Chemistry and Computer Science

Computational Studies of the 13C and 1H NMR Isotropic Chemical Shifts Using Density Functional Optimized Geometries. Adamantane and 2,4-Methano-2,4-dehydroadamantane (a [3.1.1]Propellane) as Case Studies

The 13C and 1H Chemical shift values computed at HF, BLYP and B3LYP/6-311G(d,p) levels of theory, for the BLYP/6-31G(d,p) optimized geometries of adamantane and 2,4-methano-2,4-dehydroadamantane, are reported and compared with the available experimental data. Except for the »inverted« carbon atoms, the HF values are superior to the DFT ones when the isotropic shifts with respect to TMS are in question. However, in case of the relative shifts computed with respect to the most deshielded center within the molecule, the DFT methods yield significantly better agreement with the experiment than the HF. The most probable reason for these findings may be the cancellation of errors arising from the inappropriate description of the paramagnetic contributions to the overall shielding tensor within the Kohn-Sham approach when an internal standard (within a molecule) is chosen, instead of an external one. The CSGT relative shift values correlate better with the experiment than the GIAO ones, the correlations being significantly superior at DFT than at the corresponding HF level of theory

Foreword

The MATCH/CHEM/COMP\u2798 (MCC\u2798) International Course & Conference, the thirtheenth in a series of meetings on the interfaces between mathematics, chemistry and computer sciences, was held at the Inter- University Center in Dubrovnik

Foreword

The MATCH/CHEM/COMP\u2798 (MCC\u2798) International Course & Conference, the thirtheenth in a series of meetings on the interfaces between mathematics, chemistry and computer sciences, was held at the Inter- University Center in Dubrovnik

The Difference Between the Accuracy of Real and the Corresponding Random Model is a Useful Parameter for Validation of Two-State Classification Model Quality

The simplest and the most commonly used measure for assess the classification model quality is parameter Q2 = 100 (p + n) / N (%) named the classification accuracy, p, n and N are the total numbers of correctly predicted compounds in the first and in the second class, and the total number of elements of classes (compounds) in data set, respectively. Moreover, the most probable accuracy that can be obtained by a random model is calculated for two-state model by the formulae Q2,rnd = 100 [(p + u) (p + o) + (n + u) (n + o)] / N2 (%), where u and o are the total number of under-predictions (when class 1 is predicted by the model as class 2) and over-predictions (when class 2 is predicted by the model as class 1) in data set, respectively. Finally, the difference between these two parameter ΔQ2 = Q2 – Q2,rnd is introduced, and it is suggested to compute and give ΔQ2 for each two-state classification model to assess its contribution over the accuracy of the corresponding random model. When data set is ideally balanced having the same numbers of elements in both classes, the two-state classification problem is the most difficult with maximal Q2 = 100 % and Q2,rnd = 50 %, giving the maximal ΔQ2 = 50 %. The usefulness of ΔQ2 parameter is illustrated in comparative analysis on two-class classification models from literature for prediction of secondary structure of membrane proteins and on several quanti¬tative structure-property models. Real contributions of these models over the random level of accuracy is calculated, and their ΔQ2 values are compared mutually and with the value of ΔQ2 (= 50 %) for the most difficult two-state classification model

Foreword

MATH/CHEM/COMP 2001 Dubrovnik International Course & Conference (MCC 2001