Professor Nenad Trinajstić - a Strong Supporter of Young Researchers

On the occasion of the 80th Anniversary of Prof. Dr. Nenad Trinajsti

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

Predicting inhibition of microsomal p-hydroxylation of aniline by aliphatic alcohols: A QSAR approach based on the weighted path numbers

Weighted path numbers are used to build QSAR models for predicting inhibition of microsomal p-hydroxylation of aniline by aliphatic alcohols. Models with two, three and four weighted path numbers are considered. Fit and cross-validated statistical parameters are used to measure the model quality. The best statistical parameters possess models with four weighted path numbers. Comparison with models from the literature favors models based on the weighted path numbers

Predicting Inhibition of Microsomal p-Hydroxylation of Aniline by Aliphatic Alcohols: A QSAR Approach Based on the Weighted Path Numbers

Weighted path numbers are used to build QSAR models for predicting inhibition of microsomal p-hydroxylation of aniline by aliphatic alcohols. Models with two, three and four weighted path numbers are considered. Fit and cross-validated statistical parameters are used to measure the model quality. The best statistical parameters possess models with four weighted path numbers. Comparison with models from the literature favors models based on the weighted path numbers

Harary Index - Twelve Years Later

A modification of the Harary index, denoted by H and introduced twelve years ago, is proposed. Unlike the original index, this index, called the modified Harary index and denoted by mH, consists of two parts: the first relates to greater contributions of outer bonds and the second to smaller contributions of inner bonds of an alkane, which is in accordance with the chemists’ intuition. The Wiener index W, Harary index and modified Harary index are compared in the structure-property modeling of eight representative properties of lower alkanes. The models considered were linear, Wiener-like and linear and nonlinear multivariate. Multivariate models were obtained using our variable selection procedure CROMRsel (B. Lučić and N. Trinajstić, J. Chem. Inf Comput. Sci. 39 (1999) 121-132). Multivariate models represent considerable improvements over the other two kinds of models. For example, the standard error of estimate improves on going from the best linear structure-boiling point model involving mH (S = 7.6 °C) to the best Wiener-like model based on the reduced Wiener number W/N2 and the number of paths of the length of three p3 (S = 6.2 °C) to the best four-parameter multivariate model containing ln values of W, H and mH, and p3 (S = 1.5 °C). Ali good models obtained in this work involve mH, suggesting that this index has a great potential to be used in QSPR. Its advantage over W and H is due to the fact that the main contribution to mH comes from the outer, more exposed, bonds, which is not the case of the other two indices