Quantitative structure–property relationships in pharmaceutical research – Part 2

Predicting the Thermal Stability of Nitroaromatic Compounds Using Chemoinformatic Tools

“…[5,6] These principles have already been applied in the last years for few QSPR models to the evaluation of substances toxicity in order to circumvent the ethical problems related to the animal testing. [7,8] While the popularity of QSAR methods can be certainly acknowledged within the field of biology [9] , pharmaceutics [10,11] , and toxicity predictions [12][13][14][15] , the QSPR approach may find significant applications within the REACH framework for charAbstract: In the framework of the European REACH regulation major attention was recently devoted to toxicological and ecotoxicological problems while little attention has been dedicated to other important applications concerning chemical hazards, for instance, explosive properties. In this work different chemoinformatic tools such as partial least squares, multilinear regressions, and decision trees have been used for the development of a novel quantitative structure-property relationships to predict the heat of decomposition of a series of nitroaromatic compounds.…”

Development of a QSPR model for predicting thermal stabilities of nitroaromatic compounds taking into account their decomposition mechanisms

“…Widely used in biology [13,14], toxicology [15,16] and drug design [17,18], their applications for physico-chemical properties increased since many years [19,20], particularly for the properties of energetic materials [21][22][23][24][25][26][27][28][29][30][31][32]. Their principle consists in developing a mathematic relationship connecting a macroscopic property of a compounds series to microscopic descriptors derived from their molecular structures, using a reliable experimental data set.…”

Statistical learning approach for predicting specific pharmacodynamic, pharmacokinetic, or toxicological properties of pharmaceutical agents

“…Moreover, an overall accuracy P 5 (TP 1 TN)/N, where TP and TN are the true positive and true negative, respectively, and N is the total number of agents, can also be used to indicate the overall prediction performance. The number of agents in most of the studies listed in Table 1 is in the range of hundreds up to thousands, which is significantly higher than the tens of agents typically used in QSAR and QSPR studies [Grover et al, 2000;Kubinyi, 1997].…”