Karen C. Weber

Pharmacophore-based 3D QSAR studies on a series of high affinity 5-HT1A receptor ligands

5-HT(1A) receptor antagonists have been employed to treat depression, but the lack of structural information on this receptor hampers the design of specific and selective ligands. In this study, we have performed CoMFA studies on a training set of arylpiperazines (high affinity 5-HT(1A) receptor ligands) and to produce an effective alignment of the data set, a pharmacophore model was produced using Galahad. A statistically significant model was obtained, indicating a good internal consistency and predictive ability for untested compounds. The information gathered from our receptor-independent pharmacophore hypothesis is in good agreement with results from independent studies using different approaches. Therefore, this work provides important insights on the chemical and structural basis involved in the molecular recognition of these compounds.

Two-Dimensional QSAR Studies on Arylpiperazines as High-Affinity 5-HT1A 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.

Molecular Features Related to HIV Integrase Inhibition Obtained from Structure- and Ligand-Based Approaches

Among several biological targets to treat AIDS, HIV integrase is a promising enzyme that can be employed to develop new anti-HIV agents. The aim of this work is to propose a mechanistic interpretation of HIV-1 integrase inhibition and to rationalize the molecular features related to the binding affinity of studied ligands. A set of 79 HIV-1 integrase inhibitors and its relationship with biological activity are investigated employing 2D and 3D QSAR models, docking analysis and DFT studies. Analyses of docking poses and frontier molecular orbitals revealed important features on the main ligand-receptor interactions. 2D and 3D models presenting good internal consistency, predictive power and stability were obtained in all cases. Significant correlation coefficients (r2 = 0.908 and q2 = 0.643 for 2D model; r2 = 0.904 and q2 = 0.719 for 3D model) were obtained, indicating the potential of these models for untested compounds. The generated holograms and contribution maps revealed important molecular requirements to HIV-1 IN inhibition and several evidences for molecular modifications. The final models along with information resulting from molecular orbitals, 2D contribution and 3D contour maps should be useful in the design of new inhibitors with increased potency and selectivity within the chemical diversity of the data.

Identification of electronic and structural descriptors of adenosine analogues related to inhibition of leishmanial glyceraldehyde-3-phosphate dehydrogenase.

Quantitative structure–activity relationship (QSAR) studies were performed in order to identify molecular features responsible for the antileishmanial activity of 61 adenosine analogues acting as inhibitors of the enzyme glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). Density functional theory (DFT) was employed to calculate quantum-chemical descriptors, while several structural descriptors were generated with Dragon 5.4. Variable selection was undertaken with the ordered predictor selection (OPS) algorithm, which provided a set with the most relevant descriptors to perform PLS, PCR and MLR regressions. Reliable and predictive models were obtained, as attested by their high correlation coefficients, as well as the agreement between predicted and experimental values for an external test set. Additional validation procedures were carried out, demonstrating that robust models were developed, providing helpful tools for the optimization of the antileishmanial activity of adenosine compounds.

Research Article: Insights into the Molecular Requirements for the Anti‐obesity Activity of a Series of CB1 Ligands

Two‐dimensional and 3D quantitative structure–activity relationships studies were performed on a series of diarylpyridines that acts as cannabinoid receptor ligands by means of hologram quantitative structure–activity relationships and comparative molecular field analysis methods. The quantitative structure–activity relationships models were built using a data set of 52 CB1 ligands that can be used as anti‐obesity agents. Significant correlation coefficients (hologram quantitative structure–activity relationships: r 2 = 0.91, q 2 = 0.78; comparative molecular field analysis: r 2 = 0.98, q 2 = 0.77) were obtained, indicating the potential of these 2D and 3D models for untested compounds. The models were then used to predict the potency of an external test set, and the predicted (calculated) values are in good agreement with the experimental results. The final quantitative structure–activity relationships models, along with the information obtained from 2D contribution maps and 3D contour maps, obtained in this study are useful tools for the design of novel CB1 ligands with improved anti‐obesity potency.

Selection of 2D/3D molecular descriptors and QSAR modeling of aromatic Morita–Baylis–Hillman adducts with leishmanicidal activities

Aromatic Morita–Baylis–Hillman adducts (MBHA) are a class of compounds which have shown antiparasitic potential in tropical diseases (such as Leishmaniases, Chagas disease, malaria). MBHA analogs have been studied by our research group on synthetical, theoretical, and bioactivity aspects. We present here a variable selection of 2D and 3D molecular descriptors, followed by quantitative structure–activity relationship (QSAR) modeling of thirty-two MBHA bioactive against the parasite Leishmania amazonensis. Descriptors were calculated by E-Dragon online platform. Variable selection was performed using ordered predictor selector (OPS) algorithm, and QSAR models were obtained using partial least squares (PLS). Internal and external validation parameters indicated a robust and predictive model, which may help the synthesis of most potent leishmanicidal compounds.

Pattern Recognition Techniques Applied to the Study of Leishmanial Glyceraldehyde-3-Phosphate Dehydrogenase Inhibition

Chemometric pattern recognition techniques were employed in order to obtain Structure-Activity Relationship (SAR) models relating the structures of a series of adenosine compounds to the affinity for glyceraldehyde 3-phosphate dehydrogenase of Leishmania mexicana (LmGAPDH). A training set of 49 compounds was used to build the models and the best ones were obtained with one geometrical and four electronic descriptors. Classification models were externally validated by predictions for a test set of 14 compounds not used in the model building process. Results of good quality were obtained, as verified by the correct classifications achieved. Moreover, the results are in good agreement with previous SAR studies on these molecules, to such an extent that we can suggest that these findings may help in further investigations on ligands of LmGAPDH capable of improving treatment of leishmaniasis.

New consensus multivariate models based on PLS and ANN studies of sigma-1 receptor antagonists

AbstractThe treatment of neuropathic pain is very complex and there are few drugs approved for this purpose. Among the studied compounds in the literature, sigma-1 receptor antagonists have shown to be promising. In order to develop QSAR studies applied to the compounds of 1-arylpyrazole derivatives, multivariate analyses have been performed in this work using partial least square (PLS) and artificial neural network (ANN) methods. A PLS model has been obtained and validated with 45 compounds in the training set and 13 compounds in the test set (r2training = 0.761, q2 = 0.656, r2test = 0.746, MSEtest = 0.132 and MAEtest = 0.258). Additionally, multi-layer perceptron ANNs (MLP-ANNs) were employed in order to propose non-linear models trained by gradient descent with momentum backpropagation function. Based on MSEtest values, the best MLP-ANN models were combined in a MLP-ANN consensus model (MLP-ANN-CM; r2test = 0.824, MSEtest = 0.088 and MAEtest = 0.197). In the end, a general consensus model (GCM) has been obtained using PLS and MLP-ANN-CM models (r2test = 0.811, MSEtest = 0.100 and MAEtest = 0.218). Besides, the selected descriptors (GGI6, Mor23m, SRW06, H7m, MLOGP, and μ) revealed important features that should be considered when one is planning new compounds of the 1-arylpyrazole class. The multivariate models proposed in this work are definitely a powerful tool for the rational drug design of new compounds for neuropathic pain treatment. Graphical abstractMain scaffold of the 1-arylpyrazole derivatives and the selected descriptors.

Understanding electrostatic and steric requirements related to hypertensive action of AT(1) antagonists using molecular modeling techniques.

AT1 receptor is an interesting biological target involved in several important diseases, such as blood hypertension and cardiovascular pathologies. In this study we investigated the main electrostatic and steric features of a series of AT1 antagonists related to hypertensive activity using structure and ligand-based strategies (docking and CoMFA). The generated 3D model had good internal and external consistency and was used to predict the potency of an external test set. The predicted values of pIC50 are in good agreement with the experimental results of biological activity, indicating that the 3D model can be used to predict the biological property of untested compounds. The electrostatic and steric CoMFA maps showed molecular recognition patterns, which were analyzed with structure-based molecular modeling studies (docking). The most and the least potent compounds docked into the AT1 binding site were subjected to molecular dynamics simulations with the aim to verify the stability and the flexibility of the ligand-receptor interactions. These results provided valuable insights on the electronic/structural requirements to design novel AT1 antagonists.

The Role of QSAR and Virtual Screening Studies in Type 2 Diabetes Drug Discovery

BACKGROUND Due to the increasing number of diabetes cases worldwide, there is an international concern to provide even more effective treatments to control this condition. METHODS This review brings together a selection of studies that helped to broaden the comprehension of various biological targets and associated mechanisms involved in type 2 diabetes mellitus. RESULTS Such studies demonstrated that QSAR techniques and virtual screenings have been successfully employed in drug design projects. CONCLUSIONS Therefore, the main goal of this review is to give the state-of-art for the development of new drugs for the treatment of type 2 diabetes mellitus and to evaluate how computational tools, such as virtual screening and 3D-QSAR, can aid the development of new drugs with reduced adverse side effects.