Two-dimensional QSAR studies on arylpiperazines as high-affinity 5-HT(1A) receptor ligands

5-HT(1A) receptor plays an important role in the delayed onset of antidepressant action of a class of selective serotonin reuptake inhibitors. Moreover, 5-HT(1A) receptor levels have been shown to be altered in patients suffering from major depression. In this work, hologram quantitative structure-activity relationship (HQSAR) studies were performed on a series of arylpiperazine compounds presenting affinity to the 5-HT(1A) receptor. The models were constructed with a training set of 70 compounds. The most significant HQSAR model (q(2) = 0.81, r(2) = 0.96) was generated using atoms, bonds, connections, chirality, and donor and acceptor as fragment distinction, with fragment size of 6-9. Predictions for an external test set containing 20 compounds are in good agreement with experimental results showing the robustness of the model. Additionally, useful information can be obtained from the 2D contribution maps.

Classical and hologram QSAR studies on a series of inhibitors of trypanosomatid Glyceraldehyde-3-Phosphate Dehydrogenase

Leishmaniasis and trypanosomiasis are major causes of morbidity and mortality in both tropical and subtropical regions of the world. The current available drugs are limited, ineffective, and require long treatment regimens. Due to the high dependence of trypanosomatids on glycolysis as a source of energy, some glycolytic enzymes have been identified as attractive targets for drug design. In the present work, classical Two-Dimensional Quantitative Structure -Activity Relationships (2D QSAR) and Hologram QSAR (HQSAR) studies were performed on a series of adenosine derivatives as inhibitors of Leishmania mexicana Glyceraldehyde-3-Phosphate Dehydrogenase (LmGAPDH). Significant correlation coefficients (classical QSAR, r(2)=0.83 and q(2) =0.81; HQSAR, r(2)=0.91 and q(2) =0.86) were obtained for the 56 training set compounds, indicating the potential of the models for untested compounds. The models were then externally validated using a test set of 14 structurally related compounds and the predicted values were in good agreement with the experimental results (classical QSAR, r(pred)(2) = 0.94; HQSAR, r(pred)(2) = 0.92).

Fragment-based and classical quantitative structure-activity relationships for a series of hydrazides as antituberculosis agents

Worldwide, tuberculosis (TB) is the leading cause of death among curable infectious diseases. Multidrug-resistant Mycobacterium tuberculosis is an emerging problem of great importance to public health, and there is an urgent need for new anti-TB drugs. In the present work, classical 2D quantitative structure-activity relationships (QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 91 isoniazid derivatives. Significant statistical models (classical QSAR, q(2) = 0.68 and r(2) = 0.72; HQSAR, q(2) = 0.63 and r(2) = 0.86) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 24 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, r(pred)(2) = 0.87; classical QSAR, r(pred)(2) = 0.75).

Classical and fragment-based hologram structure-activity relationships for a series of analgesic cyclic imides

Cyclic imides have been widely employed in drug design research due to their multiple pharmacological and biological properties. In the present study, two-dimensional quantitative structure-activity relationship (2D QSAR) studies were conducted on a series of potent analgesic cyclic imides using both classical and hologram QSAR (HQSAR) methods, yielding significant statistical models (classical QSAR, q(2) = 0.80; HQSAR, q(2) = 0.84). The models were then used to evaluate an external data test, and the predicted values were in good agreement with the experimental results, indicating their consistency for untested compounds.

Structure-activity relationships for a class of selective inhibitors of the major cysteine protease from Trypanosoma cruzi

Chagas` disease is a parasitic infection widely distributed throughout Latin America, with devastating consequences in terms of human morbidity and mortality. Cruzain, the major cysteine protease from Trypanosoma cruzi, is an attractive target for antitrypanosomal chemotherapy. In the present work, classical two-dimensional quantitative structure-activity relationships (2D QSAR) and hologram QSAR (HQSAR) studies were performed on a training set of 45 thiosemicarbazone and semicarbazone derivatives as inhibitors of T. cruzi cruzain. Significant statistical models (HQSAR, q2=0.75 and r2=0.96; classical QSAR, q2=0.72 and r2=0.83) were obtained, indicating their consistency for untested compounds. The models were then used to evaluate an external test set containing 10 compounds which were not included in the training set, and the predicted values were in good agreement with the experimental results (HQSAR, [image omitted]=0.95; classical QSAR, [image omitted]=0.91), indicating the existence of complementary between the two ligand-based drug design techniques.

Integration of Ligand- and Target-Based Virtual Screening for the Discovery of Cruzain Inhibitors

A myriad of methods are available for virtual screening of small organic compound databases. In this study we have successfully applied a quantitative model of consensus measurements, using a combination of 3D similarity searches (ROCS and EON), Hologram Quantitative Structure Activity Relationships (HQSAR) and docking (FRED, FlexX, Glide and AutoDock Vina), to retrieve cruzain inhibitors from collected databases. All methods were assessed individually and then combined in a Ligand-Based Virtual Screening (LBVS) and Target-Based Virtual Screening (TBVS) consensus scoring, using Receiving Operating Characteristic (ROC) curves to evaluate their performance. Three consensus strategies were used: scaled-rank-by-number, rank-by-rank and rank-by-vote, with the most thriving the scaled-rank-by-number strategy, considering that the stiff ROC curve appeared to be satisfactory in every way to indicate a higher enrichment power at early retrieval of active compounds from the database. The ligand-based method provided access to a robust and predictive HQSAR model that was developed to show superior discrimination between active and inactive compounds, which was also better than ROCS and EON procedures. Overall, the integration of fast computational techniques based on ligand and target structures resulted in a more efficient retrieval of cruzain inhibitors with desired pharmacological profiles that may be useful to advance the discovery of new trypanocidal agents.

2D QSAR and similarity studies on cruzain inhibitors aimed at improving selectivity over cathepsin L

Hologram quantitative structure-activity relationships (HQSAR) were applied to a data set of 41 cruzain inhibitors. The best HQSAR model (Q(2) = 0.77; R-2 = 0.90) employing Surflex-Sim, as training and test sets generator, was obtained using atoms, bonds, and connections as fragment distinctions and 4-7 as fragment size. This model was then used to predict the potencies of 12 test set compounds, giving satisfactory predictive R-2 value of 0,88. The contribution maps obtained from the best HQSAR model are in agreement with the biological activities of the study compounds. The Trypanosoma cruzi cruzain shares high similarity with the mammalian homolog cathepsin L. The selectivity toward cruzam was checked by a database of 123 compounds, which corresponds to the 41 cruzain inhibitors used in the HQSAR model development plus 82 cathepsin L inhibitors. We screened these compounds by ROCS (Rapid Overlay of Chemical Structures), a Gaussian-shape volume overlap filter that can rapidly identify shapes that match the query molecule. Remarkably, ROCS was able to rank the first 37 hits as being only cruzain inhibitors. In addition, the area under the curve (AUC) obtained with ROCS was 0.96, indicating that the method was very efficient to distinguishing between cruzain and cathepsin L inhibitors. (c) 2007 Elsevier Ltd. All rights reserved.

Advanced QSAR Studies on PPAR delta Ligands Related to Metabolic Diseases

PPAR delta is a nuclear receptor that, when activated, regulates the metabolism of carbohydrates and lipids and is related to metabolic syndrome and type 2 diabetes. To understand the main interactions between ligands and PPAR delta, we have constructed 2D and 3D QSAR models and compared them with HOMO, LUMO and electrostatic potential maps of the compounds studied, as well as docking results. All QSAR models showed good statistical parameters and prediction outcomes. The QSAR models were used to predict the biological activity of an external test set, and the predicted values are in good agreement with the experimental results. Furthermore, we employed all maps to evaluate the possible interactions between the ligands and PPAR delta. These predictive QSAR models, along with the HOMO, LUMO and MEP maps, can provide insights into the structural and chemical properties that are needed in the design of new PPAR delta ligands that have improved biological activity and can be employed to treat metabolic diseases.