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

Exploring different computational approaches for effective diagnosis of breast cancer

Breast cancer has been identified as one among the top causes of female death worldwide. According to recent research, earlier detection plays an important role toward fortunate medicaments and thus, decreasing the mortality rate due to breast cancer among females. This review provides a fleeting summary involving traditional diagnostic procedures from the past and today, and also modern computational tools that have greatly aided in the identification of breast cancer. Computational techniques involving different algorithms such as Support vector machines, deep learning techniques and robotics are popular among the academicians for detection of breast cancer. They discovered that Convolutional neural network was a common option for categorization among such approaches. Deep learning techniques are evaluated using performance indicators such as accuracy, sensitivity, specificity, or measure. Furthermore, molecular docking, homology modeling and Molecular dynamics Simulation gives a road map for future discussions about developing improved early detection approaches that holds greater potential in increasing the survival rate of cancer patients. The different computational techniques can be a new dominion among researchers and combating the challenges associated with breast cancer.

Curated Database and Preliminary AutoML QSAR Model for 5-HT1A Receptor

Introduction of a new drug to the market is a challenging and resource-consuming process. Predictive models developed with the use of artificial intelligence could be the solution to the growing need for an efficient tool which brings practical and knowledge benefits, but requires a large amount of high-quality data. The aim of our project was to develop quantitative structure–activity relationship (QSAR) model predicting serotonergic activity toward the 5-HT1A receptor on the basis of a created database. The dataset was obtained using ZINC and ChEMBL databases. It contained 9440 unique compounds, yielding the largest available database of 5-HT1A ligands with specified pKi value to date. Furthermore, the predictive model was developed using automated machine learning (AutoML) methods. According to the 10-fold cross-validation (10-CV) testing procedure, the root-mean-squared error (RMSE) was 0.5437, and the coefficient of determination (R²) was 0.74. Moreover, the Shapley Additive Explanations method (SHAP) was applied to assess a more in-depth understanding of the influence of variables on the model’s predictions. According to to the problem definition, the developed model can efficiently predict the affinity value for new molecules toward the 5-HT1A receptor on the basis of their structure encoded in the form of molecular descriptors. Usage of this model in screening processes can significantly improve the process of discovery of new drugs in the field of mental diseases and anticancer therapy.

Solvates of New Arylpiperazine Salicylamide Derivative-a Multi-Technique Approach to the Description of 5 HTR Ligand Structure and Interactions

A new ligand for 5-HT1A and 5-HT7 receptors, an arylpiperazine salicylamide derivative with an inflexible spacer, is investigated to identify preferred fragments capable of creating essential intermolecular interactions in different solvates. To fully identify and characterize the obtained crystalline materials, various methods including powder and single-crystal X-ray diffraction, solid-state NMR, and thermal analysis were employed, supplemented by periodic ab initio calculations. The molecular conformation in different solvates, types, and hierarchy of intermolecular interactions as well as the crystal packing were investigated to provide data for future research focused on studying protein–ligand interactions. Based on various methods of crystal structure analysis, including the interaction energy calculation and programs using an artificial neural network, a salicylamide fragment was found to be crucial for intermolecular contacts, mostly of dispersion and electrostatic character. A supramolecular 2D kite-type layer of {4,4} topology was found to form in crystals. The closed voids between layers contain disordered solvents, very weakly interacting with the molecule and the layer. It has been postulated that the separation of the layers might be influenced by an increase in temperature or the size of the solvent; hence, only methanol and ethanol hemi-solvates could be obtained from a series of various alcohols.

In silico study directed towards identification of the key structural features of GyrB inhibitors targeting MTB DNA gyrase: HQSAR, CoMSIA and molecular dynamics simulations

Mycobacterium tuberculosis DNA gyrase subunit B (GyrB) has been identified as a promising target for rational drug design against fluoroquinolone drug-resistant tuberculosis. In this study, we attempted to identify the key structural feature for highly potent GyrB inhibitors through 2D-QSAR using HQSAR, 3D-QSAR using CoMSIA and molecular dynamics (MD) simulations approaches on a series of thiazole urea core derivatives. The best HQSAR and CoMSIA models based on IC₅₀ and MIC displayed the structural basis required for good activity against both GyrB enzyme and mycobacterial cell. MD simulations and binding free energy analysis using MM-GBSA and waterswap calculations revealed that the urea core of inhibitors has the strongest interaction with Asp79 via hydrogen bond interactions. In addition, cation-pi interaction and hydrophobic interactions of the R₂ substituent with Arg82 and Arg141 help to enhance the binding affinity in the GyrB ATPase binding site. Thus, the present study provides crucial structural features and a structural concept for rational design of novel DNA gyrase inhibitors with improved biological activities against both enzyme and mycobacterial cell, and with good pharmacokinetic properties and drug safety profiles.

From Homology Models to a Set of Predictive Binding Pockets-a 5-HT1A Receptor Case Study

Despite its remarkable importance in the arena of drug design, serotonin 1A receptor (5-HT1A) has been elusive to the X-ray crystallography community. This lack of direct structural information not only hampers our knowledge regarding the binding modes of many popular ligands (including the endogenous neurotransmitter-serotonin), but also limits the search for more potent compounds. In this paper we shed new light on the 3D pharmacological properties of the 5-HT1A receptor by using a ligand-guided approach (ALiBERO) grounded in the Internal Coordinate Mechanics (ICM) docking platform. Starting from a homology template and set of known actives, the method introduces receptor flexibility via Normal Mode Analysis and Monte Carlo sampling, to generate a subset of pockets that display enriched discrimination of actives from inactives in retrospective docking. Here, we thoroughly investigated the repercussions of using different protein templates and the effect of compound selection on screening performance. Finally, the best resulting protein models were applied prospectively in a large virtual screening campaign, in which two new active compounds were identified that were chemically distinct from those described in the literature.

Chemical Structure-Biological Activity Models for Pharmacophores' 3D-Interactions

Within medicinal chemistry nowadays, the so-called pharmaco-dynamics seeks for qualitative (for understanding) and quantitative (for predicting) mechanisms/models by which given chemical structure or series of congeners actively act on biological sites either by focused interaction/therapy or by diffuse/hazardous influence. To this aim, the present review exposes three of the fertile directions in approaching the biological activity by chemical structural causes: the special computing trace of the algebraic structure-activity relationship (SPECTRAL-SAR) offering the full analytical counterpart for multi-variate computational regression, the minimal topological difference (MTD) as the revived precursor for comparative molecular field analyses (CoMFA) and comparative molecular similarity indices analysis (CoMSIA); all of these methods and algorithms were presented, discussed and exemplified on relevant chemical medicinal systems as proton pump inhibitors belonging to the 4-indolyl,2-guanidinothiazole class of derivatives blocking the acid secretion from parietal cells in the stomach, the 1-[(2-hydroxyethoxy)-methyl]-6-(phenylthio)thymine congeners’ (HEPT ligands) antiviral activity against Human Immunodeficiency Virus of first type (HIV-1) and new pharmacophores in treating severe genetic disorders (like depression and psychosis), respectively, all involving 3D pharmacophore interactions.

Quantitative Structure-Activity Relationship for High Affinity 5-HT1A Receptor Ligands Based on Norm Indexes

Arylpiperazine derivatives are promising 5-hydroxytryptamine (5-HT) receptor ligands which can inhibit serotonin reuptake effectively. In this work, some norm index descriptors were proposed and further utilized to develop a model for predicting 5-HT1A receptor affinity (pKi) of 88 arylpiperazine derivatives. Results showed that this new model could provide satisfactory predictions with the square of the correction coefficient (R(2)) of 0.8891 and the squared correlation coefficient of cross-validation (Q(2)) of 0.8082, respectively. In addition, the applicability domain of this model was validated by using the leverage approach and results which suggested potential large scale for further utilization of this model. The results of statistical values and validation tests demonstrated that our proposed norm index based model could be successfully applied for predicting the affinity 5-HT1A receptor ligands of arylpiperazine derivatives.

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.

A linear combination of pharmacophore hypotheses as a new tool in search of new active compounds--an application for 5-HT1A receptor ligands

This study explores a new approach to pharmacophore screening involving the use of an optimized linear combination of models instead of a single hypothesis. The implementation and evaluation of the developed methodology are performed for a complete known chemical space of 5-HT_1AR ligands (3616 active compounds with K_i < 100 nM) acquired from the ChEMBL database. Clusters generated from three different methods were the basis for the individual pharmacophore hypotheses, which were assembled into optimal combinations to maximize the different coefficients, namely, MCC, accuracy and recall, to measure the screening performance. Various factors that influence filtering efficiency, including clustering methods, the composition of test sets (random, the most diverse and cluster population-dependent) and hit mode (the compound must fit at least one or two models from a final combination) were investigated. This method outmatched both single hypothesis and random linear combination approaches.

Inhibitors of Trypanosoma brucei trypanothione reductase: comparative molecular field analysis modeling and structural basis for selective inhibition

Background: Sleeping sickness is a major cause of death in Africa. Since no secure treatment is available, the development of novel therapeutic agents is urgent. In this context, the enzyme trypanothione reductase (TR) is a prominent molecular target that has been investigated in drug design for sleeping sickness. Results: In this study, comparative molecular field analysis models were generated for a series of Trypanosoma brucei TR inhibitors. Statistically significant results were obtained and the models were applied to predict the activity of external test sets, with good correlation between predicted and experimental results. We have also investigated the structural requirements for the selective inhibition of the parasite‘s enzyme over the human glutathione reductase. Conclusion: The quantitative structure–activity relationship models provided valuable information regarding the essential molecular requirements for the inhibitory activity upon the target protein, providing important insights into the design of more potent and selective TR inhibitors.

5-HT1A receptor pharmacophores to screen for off-target activity of α1-adrenoceptor antagonists

The α1-adrenoceptors (α1-ARs), in particular the α1A-AR subtype, are current therapeutic targets of choice for the treatment of urogenital conditions, such as benign prostatic hyperplasia (BPH). Due to the similarity between the transmembrane domains of the α1-AR subtypes, and the serotonin receptor subtype 1A (5-HT1A-R), currently used α1-AR subtype-selective drugs to treat BPH display considerable off-target affinity for the 5-HT1A-R, leading to side effects. We describe the construction and validation of pharmacophores for 5-HT1A-R agonists and antagonists. Through the structural diversity of the training sets used in their development, these pharmacophores define the properties of a compound needed to bind to 5-HT1A receptors. Using these and previously published pharmacophores in virtual screening and profiling, we have identified unique chemical compounds (hits) that fit the requirements to bind to our target, the α1A-AR, selectively over the off-target, the 5-HT1A-R. Selected hits have been obtained and their affinities for α1A-AR, α1B-AR and 5-HT1A-R determined in radioligand binding assays, using membrane preparations which contain human receptors expressed individually. Three of the tested hits demonstrate statistically significant selectivity for α1A-AR over 5-HT1A-R. All seven tested hits bind to α1A-AR, with two compounds displaying K i values below 1 μM, and a further two K i values of around 10 μM. The insights and knowledge gained through the development of the new 5-HT1A-R pharmacophores will greatly aid in the design and synthesis of derivatives of our lead compound, and allow the generation of more efficacious and selective ligands.

Pharmacophore and QSAR studies to design novel histone deacetylase 2 inhibitors

One pharmacophore model and three quantitative structure-activity relationship models were developed on a series of benzimidazole and imidazole inhibitors of histone deacetylase 2. The goodness of hit score value of the best pharmacophore model was 0.756, which indicated that it is reliable to be used for virtual screening. The built pharmacophore model was used to search the NCI database. The hit compounds were subjected to molecular docking. The results showed that 25 compounds had high scores and strong interactions with histone deacetylase 2. In three-dimensional quantitative structure-activity relationship studies, good predictive models were obtained using comparative molecular field analysis, comparative molecular similarity indices analysis, and Topomer comparative molecular field analysis. Some putative active compounds were proposed based on compound no. 41. Twenty-six compounds had high scores and good interactions when they were docking into histone deacetylase 2.

Computational methods for the discovery of mood disorder therapies

INTRODUCTION

Despite the significant progress, research is still needed to reveal details of the complex and dynamic chemical processes operating in the central nervous system (CNS) and their relationship to psychological effects such as mood disorders. The incidence of behavioral depression is widely spread worldwide, with an estimated 14.8 million adults diagnosed yearly in the United States alone. The efficacy of current antidepressants on 50 - 60% of patients, their slow onset of action and the prevalence of adverse side effects highlight the need for developing a new generation of improved antidepressants. Computational methods have the potential to aid in the discovery of mood modulators.

AREAS COVERED

This review contains three main sections: historical evolution of marketed antidepressants, physicochemical and structural properties of antidepressant compounds reported in the ChEMBL database and recent efforts in the design and discovery of antidepressants using computational methods. The authors provide details of the computational methods employed, from chemoinformatic analyses to molecular modeling.

EXPERT OPINION

While there have been numerous and important findings in depression research, the high cost and time spent on research into new therapies for brain disorders is a risky undertaking. Computational methodologies can be employed to speed up the discovery of new antidepressants and to detect new sources of chemical compounds with potential antidepressant activity. Compound collections containing compounds already approved in the pharmaceutical and food industries that cover the property space and complement the structural space of CNS drugs represent a promising starting point for the discovery of new antidepressant agents.