Prediction of physico-chemical properties for REACH based on QSPR models

International audienceQuantitative Structure Property Relationship models have been developed for the prediction of flash points of two families of organic compounds selected in the PREDIMOL French Project: amines and organic peroxides. If the model dedicated to amines respected all OECD validation principles with excellent performances in predictivity, the one dedicated to organic peroxides was not validated on an external validation set, due to the low number of available data, but already presented high performances in fitting and robustness. This work highlighted the need of gathering experimental data, as in progress in the PREDIMOL project, to achieve validated reliable models that could be used in a regulatory framework, like REACH. Such models are expected to be submitted to the European Joint Research Comity (JRC) and to existing tools (like the OECD ECHA QSAR Toolbox) to be available for use by industrials and regulatory instances

QSAR and Molecular Docking Studies on a Series of Cinnamic Acid Analogues as Epidermal Growth Factor Receptor (EGFR) Inhibitors

Quantitative structure-activity relationship (QSAR) and docking studies have been performed on a large series of cinnamic acid analogues studied by various authors as Epidermal Growth Factor Receptor (EGFR) inhibitors. A multiple linear regression (MLR) analysis has shown that electronic properties of these compounds are the governing factors of their activity and docking study has shown that compounds can form hydrogen bonds with the receptor and have effective steric interactions involving dispersion forces. Using the MLR model, some new compounds were proposed that have higher potency than the existing ones.Declared non

Modeling of mannich bases fungicidal activity by the MLR approach

In the present paper, we have carried out quantitative structure-fungicidal activity relationships analysis on a novel series of Mannich bases with trifluoromethyl-1,2,4-triazole and substituted benzylpiperazine moieties reported to have improved fungicidal activity against Fusarium oxysporum f.sp. cucumerinum. The chemical structures were energy minimized based on semiempirical quantum chemical method RM1. The molecular descriptors were calculated using the DRAGON, InstantJchem and ChemProp software. Several models for the prediction of fungicidal activity have been drawn up by using the multiple regression technique (MLR). The genetic algorithm approach was employed for variable selection method to search for the best ranking models. The predictive ability of the MLR models was validated using an external test set of 5 out of 18 molecules. The best MLR model was chosen by observing acceptable r2 2 adj r and 2 LOO q values, low residual errors and high Multi-Criteria Decision Making (MCDM) scores. The MLR equation suggests the positive impact of GETAWAY and edge adjacency matrix descriptors on the fungicidal activity. The high acidic character of the molecule increase the fungicidal activity

Reviewing Ligand-Based Rational Drug Design: The Search for an ATP Synthase Inhibitor

Following major advances in the field of medicinal chemistry, novel drugs can now be designed systematically, instead of relying on old trial and error approaches. Current drug design strategies can be classified as being either ligand- or structure-based depending on the design process. In this paper, by describing the search for an ATP synthase inhibitor, we review two frequently used approaches in ligand-based drug design: The pharmacophore model and the quantitative structure-activity relationship (QSAR) method. Moreover, since ATP synthase ligands are potentially useful drugs in cancer therapy, pharmacophore models were constructed to pave the way for novel inhibitor designs

Consistency of QSAR models: Correct split of training and test sets, ranking of models and performance parameters

<div><p>Recent implementations of QSAR modelling software provide the user with numerous models and a wealth of information. In this work, we provide some guidance on how one should interpret the results of QSAR modelling, compare and assess the resulting models, and select the best and most consistent ones. Two QSAR datasets are applied as case studies for the comparison of model performance parameters and model selection methods. We demonstrate the capabilities of sum of ranking differences (SRD) in model selection and ranking, and identify the best performance indicators and models. While the exchange of the original training and (external) test sets does not affect the ranking of performance parameters, it provides improved models in certain cases (despite the lower number of molecules in the training set). Performance parameters for external validation are substantially separated from the other merits in SRD analyses, highlighting their value in data fusion.</p></div