Current Computer - Aided Drug Design - Volume 10, Issue 2, 2014

The present work employs 152 benzene-carboxylic acid' esters having computed the toxicity within the range [2.251, 10.222] for fathead minnow fish (Pimephales promelas). Calibration set includes many pairs having very similar chemical structure, size, shape and hydrophilicity, but very different value of ECOSAR toxicity or vice versa. The QSTR study, which uses all esters as calibration set, emphasized a large percent (16.2%) of outliers. In this QSTR study most of the estimated values of toxicity for outliers are much lower than ECOSAR toxicity. The LogP and some aromaticity descriptors are predictors. The best QSTR for esters having low value (< 5.5) of ECOSAR toxicity and the best QSTR for esters having high value (> 5.5) of ECOSAR toxicity are obtained when the number of outliers is very small. These QSTRs are different enough and highlight opposite influences of certain descriptors on toxicity. The results emphasize two possibilities: (a) the esters having low value of ECOSAR toxicity and the esters having high value of ECOSAR toxicity are included in two different classes from the point of view of structure-toxicity relationship and/or (b) many high values of ECOSAR toxicity are wrong. By comparison, a QSTR using experimental values of toxicity against rats for 37 benzene-carboxylic esters included in the same database gives good correlation experimental/computed values of toxicity, the number of outliers is null and the result of validation test is good.

Discerning between the concepts of difficulty and usefulness of a molecular ranking classification is of significant importance in virtual design chemistry. Here, both concepts are viewed from the statistical and practical point of view according to the standard definitions of enrichment and statistical significance p-values. These parameters are useful not only to compare distinct rankings obtained for the same molecular database, but also in order to compare the ones established in distinct molecular sets from an objective point of view.

Quantitative Structure-Activity Relationships based on molecular descriptors calculated with correlation weights within the hypermolecule, considered to mimic the investigated correlational space, was performed on a set of 40 flavonoids (PubChem database). The best models describing log P and LD50 of this set of flavonoids were validated by the leave-one-out procedure, in the external test set and in a new version of prediction by using clusters of similar molecules. The best prediction was provided by the similarity cluster procedure.

The main purpose of the present review is to summarize the most significant works up to date in the field of multi-target QSAR (mt-QSAR), in order to emphasize the importance that this technique has acquired over the last decade. Unlike traditional QSAR techniques, mt-QSAR permits to calculate the probability of activity of a given compound against different biological or pharmacological targets. In simple terms, a single equation for multiple outputs. To emphasize more the importance of the mt-QSAR in the field of drug discovery, we also present a novel mt-QSAR model, made on purpose by our research group, for the prediction of the susceptibility of Gram + and Gram – anaerobic bacteria.

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.

Drug discovery is mostly guided by innovative and knowledge by the application of experimental and computational approaches. Quantitative structure-activity relationships (QSAR) have a critical task in the discovery and optimization of lead compounds, thereby contributing to the development of new chemical entities. 3D-QSAR methods use the information of the tridimensional molecular structure of ligands and can be applied to elucidate the relationships between 3D molecular interactions and their measured biological property, therefore, providing a rational approach for the development of new potential compounds. The purpose of this review is to provide a perspective of the utility of 3DQSAR approaches in drug design, focusing on progress, challenges and future orientations. The essential steps involved to generate reliable and predictive CoMFA models are discussed. Moreover, we present an example of application of a CoMFA study to derive 3D-QSAR models for a series of oxadiazoles inhibitors of Schistosoma mansoni Thioredoxin Glutathione Reductase (SmTGR).

Lacosamide is an anticonvulsant drug which presents carbonic anhydrase inhibition. In this paper, we analyzed the apparent relationship between both activities performing a molecular modeling, docking and QSAR studies on 18 lacosamide derivatives with known anticonvulsant activity. Docking results suggested the zinc-binding site of carbonic anhydrase is a possible target of lacosamide and lacosamide derivatives making favorable Van der Waals interactions with Asn67, Gln92, Phe131 and Thr200. The mathematical models revealed a poor relationship between the anticonvulsant activity and molecular descriptors obtained from DFT and docking calculations. However, a QSAR model was developed using Dragon software descriptors. The statistic parameters of the model are: correlation coefficient, R=0.957 and standard deviation, S=0.162. Our results provide new valuable information regarding the relationship between both activities and contribute important insights into the essential molecular requirements for the anticonvulsant activity.

In normal cells, the accuracy of chromosome segregation which assures cells euploidy depends on mitosis mechanics and on proper functioning of a specific complex of proteins represented by the error-checking spindle assembly checkpoint (SAC). SAC proteins are deeply involved in correct cell divisions, but some of these, such as mitotic arrest-deficient proteins (Mad1 and Mad2), are critical. Mad1 and Mad2 are involved in preventing “wrong” cellular divisions which lead to cellular aneuploidy and are recognized as inductors of genetic disorders, as well as activators of oncoproteins. To clarify aneuploidy involvement in the evolution of cancer or other genetic disorders, structural and functional specificity of spindle checkpoint proteins have been analyzed, but the process is still poorly understood. In order to better understand SAC proteins involvement in initiation of cancer and other genetic disorders, here we review studies that conducted to relevant structural and functional information regarding these proteins. The results of these studies suggest that minor changes in structure and functionality of SAC proteins are able to generate aneuploidy. Therefore, a deeper understanding of Mad1 and Mad2 structural changes obtained by experimental and theoretical studies could open new perspectives of genetic medicine.