In silico studies for the rational discovery of anticonvulsant compounds

Theoretical models to virtual screening and rational design of anticonvulsant compounds based on a topological sub-structural molecular design (TOSS-MODE) approach are developed. These models, developed on the basis of data sets of great structural variability, permit the classification of compounds as active/inactive anticonvulsants and predict the quantitative anticonvulsant potency of such compounds. The classification model is applied to a virtual screening of anticonvulsant compounds by analyzing a data set of molecules reported in the literature. More than 88% of them were well classified by the current model. Active and inactive fragments are identified by using the present approach. Some of the active fragments are identified in anticonvulsant molecules as potential pharmacophores and one of them is analyzed in detail. The three-dimensional (3-D) features of this fragment are investigated in a series of five anticonvulsant compounds. Some structure–anticonvulsant activity relationships are derived on the basis of the 3-D structure of this fragment and some findings reported in the literature that indicate that it is an important pharamacophore are outlined.     

Non-stochastic and stochastic linear indices of the 'molecular pseudograph's atom adjacency matrix': application to 'in silico' studies for the rational discovery of new antimalarial compounds

Malaria is one of the most deadly diseases, affecting million of people especially in developing countries. Because of the rapidly increasing threat worldwide of malaria epidemics multidrugs resistant to therapies, there is an urgent global need to discover new classes of antimalarial compounds. In an effort to overcome this problem, we have investigated the use of structure-based classification models for the 'rational' selection/identification or design/optimization of new lead antimalarials from virtual combinatorial data sets. In this sense, TOpological MOlecular COMputer Design strategy (TOMOCOMD approach) has been introduced in order to obtain two quantitative models for the discrimination of antimalarials. A collected data set containing 597 antimalarial compounds is presented as a helpful tool not only for theoretical chemist but for other researchers in this area. The validated models (including non-stochastic and stochastic indices) classify correctly more than 90% of compounds in both training and external prediction data sets. They showed high Matthews' correlation coefficients; 0.87 and 0.82 for training and 0.86 and 0.79 for test set. The TOMOCOMD-CARDD approach implemented in this work was successfully compared with two of the most useful models for antimalarials selection reported so far. Thus we expect that these two QSAR models can be used in the identification of previously un-known antimalarials compounds.     

Quantitative structure-activity relationship to predict toxicological properties of benzene derivative compounds

TOPological Sub-structural MOlecular DEsign (TOPS-MODE) was used to assess acute aquatic toxicity of a series of 69 benzene derivatives. The obtained model was able to explain more than 88% of data variance, stressing the importance of molecule hydrophobicity and its dipolar moment, as well as the distance between their bonds to describe the property under study. On the other hand, this model was better than those obtained with Dragon software (Constitutional, Galvez topological charges indices and BCUT) using the same number of variables. This approach proved to be a very good method to assess acute aquatic toxicity of these king of compounds, which could be applied to other series of substances.     

New tyrosinase inhibitors selected by atomic linear indices-based classification models

In the present report, the use of the atom-based linear indices for finding functions that discriminate between the tyrosinase inhibitor compounds and inactive ones is presented. In this sense, discriminant models were applied and globally good classifications of 93.51% and 92.46% were observed for non-stochastic and stochastic linear indices best models, respectively, in the training set. The external prediction sets had accuracies of 91.67% and 89.44%. In addition, these fitted models were used in the screening of new cycloartane compounds isolated from herbal plants. A good behavior is shown between the theoretical and experimental results. These results provide a tool that can be used in the identification of new tyrosinase inhibitor compounds.     

Topological models for prediction of anti-inflammatory activity of N-arylanthranilic acids

Relationship of anti-inflammatory activity of N-arylanthranilic acids with distance based Wiener's index, adjacency based Zagreb indices M1 and M2, and distance-cum-adjacency based eccentric connectivity index (ECI) was investigated. A dataset comprising of 112 N-arylanthranilic acids was selected. The values of all the four indices for each of the 112 compounds were calculated using an in-house computer program. The dataset was divided randomly into training and test sets. The data was analyzed and suitable models were developed after identification the active ranges in the training set. Subsequently, a biological activity was assigned to each of the compound involved in the test set using these models, which was then compared with the reported anti-inflammatory activity. High accuracy of prediction ranging from 83% to 90% was observed using models based upon topological indices.     

Markovian chemicals "in silico" design (MARCH-INSIDE), a promising approach for computer aided molecular design II: experimental and theoretical assessment of a novel method for virtual screening of fasciolicides

A novel method for in silico selection of fluckicidal drugs is introduced. Two QSARs that permit us to discriminate between fasciolicide and non-fasciolicide drugs (the first) and to outline some conclusions about the possible mechanism of action of a chemical (the second) are performed. The first model correctly classified 93.85% of compounds in the training series and 89.5% of the compounds in the predicting one. This model correctly classified 87.7, 93.8, 92.2 and 93.9% of compounds in leave- n-out cross validation procedures when n takes values from 2 to until 6. The model seems to be stable in around 92% of good classification in leave- n-out cross validation analysis when n>6. The second model correctly classified 70% of non-fasciolicide compounds, 85.71% of beta-tubulin inhibitors and 100% of proton ionophores in the training set. This model recognizes as proton ionophores 100% of any nitrosalicylanilides in the predicting series. Both models have a low p-level <0.05. Finally, the experimental assay of six organic chemicals by an in vivo test permit us to carry out an assessment of the model with a fairly good 100% agreement between experiment and theoretical prediction.     

Enrichment of extremely noisy high-throughput screening data using a naïve Bayes classifier

The noise level of a high-throughput screening (HTS) experiment depends on various factors such as the quality and robustness of the assay itself and the quality of the robotic platform. Screening of compound mixtures is noisier than screening single compounds per well. A classification model based on na?ve Bayes (NB) may be used to enrich such data. The authors studied the ability of the NB classifier to prioritize noisy primary HTS data of compound mixtures (5 compounds/well) in 4 campaigns in which the percentage of noise presumed to be inactive compounds ranged between 81% and 91%. The top 10% of the compounds suggested by the classifier captured between 26% and 45% of the active compounds. These results are reasonable and useful, considering the poor quality of the training set and the short computing time that is needed to build and deploy the classifier.