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Applicability Domain (applicability + domain)

Distribution by Scientific Domains

Chemistry	100%

Selected Abstracts

Are Mechanistic and Statistical QSAR Approaches Really Different?

MOLECULAR INFORMATICS, Issue 6-7 2010
MLR Studies on 158 Cycloalkyl-Pyranones
Abstract Two parallel approaches for quantitative structure-activity relationships (QSAR) are predominant in literature, one guided by mechanistic methods (including read-across) and another by the use of statistical methods. To bridge the gap between these two approaches and to verify their main differences, a comparative study of mechanistically relevant and statistically relevant QSAR models, developed on a case study of 158 cycloalkyl-pyranones, biologically active on inhibition (Ki) of HIV protease, was performed. Firstly, Multiple Linear Regression (MLR) based models were developed starting from a limited amount of molecular descriptors which were widely proven to have mechanistic interpretation. Then robust and predictive MLR models were developed on the same set using two different statistical approaches unbiased of input descriptors. Development of models based on Statistical I method was guided by stepwise addition of descriptors while Genetic Algorithm based selection of descriptors was used for the Statistical II. Internal validation, the standard error of the estimate, and Fisher's significance test were performed for both the statistical models. In addition, external validation was performed for Statistical II model, and Applicability Domain was verified as normally practiced in this approach. The relationships between the activity and the important descriptors selected in all the models were analyzed and compared. It is concluded that, despite the different type and number of input descriptors, and the applied descriptor selection tools or the algorithms used for developing the final model, the mechanistical and statistical approach are comparable to each other in terms of quality and also for mechanistic interpretability of modelling descriptors. Agreement can be observed between these two approaches and the better result could be a consensus prediction from both the models. [source]

Principles of QSAR models validation: internal and external

MOLECULAR INFORMATICS, Issue 5 2007
Paola Gramatica
Abstract The recent REACH Policy of the European Union has led to scientists and regulators to focus their attention on establishing general validation principles for QSAR models in the context of chemical regulation (previously known as the Setubal, nowadays, the OECD principles). This paper gives a brief analysis of some principles: unambiguous algorithm, Applicability Domain (AD), and statistical validation. Some concerns related to QSAR algorithm reproducibility and an example of a fast check of the applicability domain for MLR models are presented. Common myths and misconceptions related to popular techniques for verifying internal predictivity, particularly for MLR models (for instance cross-validation, bootstrap), are commented on and compared with commonly used statistical techniques for external validation. The differences in the two validating approaches are highlighted, and evidence is presented that only models that have been validated externally, after their internal validation, can be considered reliable and applicable for both external prediction and regulatory purposes. [source]

In silico prediction and screening of ,-secretase inhibitors by molecular descriptors and machine learning methods

JOURNAL OF COMPUTATIONAL CHEMISTRY, Issue 6 2010
Xue-Gang Yang
Abstract ,-Secretase inhibitors have been explored for the prevention and treatment of Alzheimer's disease (AD). Methods for prediction and screening of ,-secretase inhibitors are highly desired for facilitating the design of novel therapeutic agents against AD, especially when incomplete knowledge about the mechanism and three-dimensional structure of ,-secretase. We explored two machine learning methods, support vector machine (SVM) and random forest (RF), to develop models for predicting ,-secretase inhibitors of diverse structures. Quantitative analysis of the receiver operating characteristic (ROC) curve was performed to further examine and optimize the models. Especially, the Youden index (YI) was initially introduced into the ROC curve of RF so as to obtain an optimal threshold of probability for prediction. The developed models were validated by an external testing set with the prediction accuracies of SVM and RF 96.48 and 98.83% for ,-secretase inhibitors and 98.18 and 99.27% for noninhibitors, respectively. The different feature selection methods were used to extract the physicochemical features most relevant to ,-secretase inhibition. To the best of our knowledge, the RF model developed in this work is the first model with a broad applicability domain, based on which the virtual screening of ,-secretase inhibitors against the ZINC database was performed, resulting in 368 potential hit candidates. © 2009 Wiley Periodicals, Inc. J Comput Chem, 2010 [source]

Estimation of Aqueous-Phase Reaction Rate Constants of Hydroxyl Radical with Phenols, Alkanes and Alcohols

MOLECULAR INFORMATICS, Issue 11-12 2009
Ya-nan Wang
Abstract A quantitative structure activity relationship (QSAR) model was developed for the aqueous-phase hydroxyl radical reaction rate constants (kOH) employing quantum chemical descriptors and multiple linear regressions (MLR). The QSAR development followed the OECD guidelines, with special attention to validation, applicability domain (AD) and mechanistic interpretation. The established model yielded satisfactory performance: the correlation coefficient square (R2) was 0.905, the root mean squared error (RMSE) was 0.139, the leave-many-out cross-validated QLMO2 was 0.806, and the external validated QEXT2 was 0.922 log units. The AD of the model covering compounds of phenols, alkanes and alcohols, was analyzed by Williams plot. The main molecular structural factors governing kOH are the energy of the highest occupied molecular orbital (EHOMO), average net atomic charges on hydrogen atoms (), molecular surface area (MSA) and dipole moment (,). It was concluded that kOH increased with increasing EHOMO and MSA, while decreased with increasing and ,. [source]