Showing papers by "Viviana Consonni published in 2014"

QSAR Modeling: Where have you been? Where are you going to?

Abstract: Quantitative structure–activity relationship modeling is one of the major computational tools employed in medicinal chemistry. However, throughout its entire history it has drawn both praise and criticism concerning its reliability, limitations, successes, and failures. In this paper, we discuss (i) the development and evolution of QSAR; (ii) the current trends, unsolved problems, and pressing challenges; and (iii) several novel and emerging applications of QSAR modeling. Throughout this discussion, we provide guidelines for QSAR development, validation, and application, which are summarized in best practices for building rigorously validated and externally predictive QSAR models. We hope that this Perspective will help communications between computational and experimental chemists toward collaborative development and use of QSAR models. We also believe that the guidelines presented here will help journal editors and reviewers apply more stringent scientific standards to manuscripts reporting new QSAR stu...

Prediction of acute aquatic toxicity toward Daphnia magna by using the GA-kNN method.

Abstract: In this study, a QSAR model was developed from a data set consisting of 546 organic molecules, to predict acute aquatic toxicity toward Daphnia magna. A modified k-Nearest Neighbour (kNN) strategy was used as the regression method, which provided prediction only for those molecules with an average distance from the k nearest neighbours lower than a selected threshold. The final model showed good performance (R(2) and Q(2) cv equal to 0.78, Q(2) ext equal to 0.72). It comprised eight molecular descriptors that encoded information about lipophilicity, the formation of H-bonds, polar surface area, polarisability, nucleophilicity and electrophilicity.

Towards Global QSAR Model Building for Acute Toxicity: Munro Database Case Study

Abstract: A series of 436 Munro database chemicals were studied with respect to their corresponding experimental LD50 values to investigate the possibility of establishing a global QSAR model for acute toxicity. Dragon molecular descriptors were used for the QSAR model development and genetic algorithms were used to select descriptors better correlated with toxicity data. Toxic values were discretized in a qualitative class on the basis of the Globally Harmonized Scheme: the 436 chemicals were divided into 3 classes based on their experimental LD50 values: highly toxic, intermediate toxic and low to non-toxic. The k-nearest neighbor (k-NN) classification method was calibrated on 25 molecular descriptors and gave a non-error rate (NER) equal to 0.66 and 0.57 for internal and external prediction sets, respectively. Even if the classification performances are not optimal, the subsequent analysis of the selected descriptors and their relationship with toxicity levels constitute a step towards the development of a global QSAR model for acute toxicity.

Assessing the validity of QSARs for ready biodegradability of chemicals: an applicability domain perspective.

Abstract: Several classical and two recently proposed Applicability Domain (AD) approaches were implemented on a set of three classification models retrieved from a published study to assess the ready biodegradability of chemicals. Each model was associated with an optimal AD approach based on its ability to a) retain maximum test molecules within the model's AD, b) be appropriate for the strategy used towards model development and c) show reasonably converging results with those derived with other AD approaches used. A decision criterion was also set to evaluate the AD of two consensus models that were developed in the original study. An overview of test molecules excluded from the AD of all the five biodegradability models was provided including an attempt to identify the major structural features and molecular descriptors possibly relevant in deciding upon their ready biodegradability. Apart from the test set, an overview of the results derived on the external validation set molecules was provided.

Validation and extension of a similarity-based approach for prediction of acute aquatic toxicity towards Daphnia magna.

Abstract: Quantitative structure–activity relationship (QSAR) models for predicting acute toxicity to Daphnia magna are often associated with poor performances, urging the need for improvement to meet REACH requirements. The aim of this study was to evaluate the accuracy, stability and reliability of a previously published QSAR model by means of further external validation and to optimize its performance by means of extension to new data as well as a consensus approach. The previously published model was validated with a large set of new molecules and then compared with ChemProp model, from which most of the validation data were taken. Results showed better performance of the proposed model in terms of accuracy and percentage of molecules outside the applicability domain. The model was re-calibrated on all the available data to confirm the efficacy of the similarity-based approach. The extended dataset was also used to develop a novel model based on the same similarity approach but using binary fingerprints to desc...

Qspr study of rheological and mechanical properties of chloroprene rubber accelerators

Abstract: In this preliminary study, mathematical models based on Quantitative Structure Property Relationships (QSPR) were applied in order to analyze how molecular structure of chloroprene rubber accelerators relates to their rheological and mechanical properties. QSPR models were developed in order to disclose which structural features mainly affect the mechanism of vulcanization. In such a way QSPR can help in a faster and more parsimonious design of new chloroprene rubber curative molecules. Regression mathematical models were calibrated on two rheological properties (scorch time and optimum cure time) and three mechanical properties (modulus 100%, hardness, and elongation at break). Models were calculated using experimental values of 14 accelerators belonging to diverse chemical classes and validated by means of different strategies. All the derived models gave a good degree of fitting (R2 values ranging from 84.5 to 98.7) and a satisfactory predictive power. Moreover, some hypotheses on the correlat...