Developing consensus 3D-QSAR and pharmacophore models for several beta-secretase, farnesyl transferase and histone deacetylase inhibitors

Exploring the Efficacy of Noncovalent SARS-CoV-2 Main Protease Inhibitors: A Computational Simulation Analysis Study

The SARS-CoV-2 main protease, as a key target for antiviral therapeutics, is instrumental in maintaining virus stability, facilitating translation, and enabling the virus to evade innate immunity. Our research focused on designing non-covalent inhibitors to counteract the action of this protease. Utilizing a 3D-QSAR model and contour map, we successfully engineered eight novel non-covalent inhibitors. Further evaluation and comparison of these novel compounds through methodologies including molecular docking, ADMET analysis, frontier molecular orbital studies, molecular dynamics simulations, and binding free energy revealed that the inhibitors N02 and N03 demonstrated superior research performance (N02 ΔGbind=-206.648 kJ/mol, N03 ΔGbind=-185.602 kJ/mol). These findings offer insightful guidance for the further refinement of molecular structures and the development of more efficacious inhibitors. Consequently, future investigations can draw upon these findings to unearth more potent inhibitors, thereby amplifying their impact in the treatment and prevention of associated diseases.

Interaction mechanism exploration of HEA derivatives as BACE1 inhibitors by in silico analysis

The β-site amyloid precursor protein cleaving enzyme 1 (BACE1) initiates the generation of β-amyloid (Aβ) peptides which play a critical early role in the pathogenesis of Alzheimer's disease (AD), and thus it is a prime target for lowering the Aβ levels to treat AD.

A Novel Strategy of Structural Similarity Based Consensus Modeling

A novel strategy of "structural similarity based consensus modeling" (SSCM) based on "model distance and guided model selection" (MD-QGMS) submodel set was proposed. The SSCM strategy is built upon a hypothesis, that is, similar compounds are most probably predicted more accurately by a same submodel among a model population, which can be concluded from the fact that models employing a different set of descriptors can predict compounds with specific structures more accurately. It is proved that the proposed SSCM strategy can remarkably improve the external prediction ability of QSAR models by employing two different datasets. In future, the proposed SSCM strategy may provide a new direction to develop more accurate predictive models.

QSAR methods for the discovery of new inflammatory bowel disease drugs

INTRODUCTION

Inflammatory bowel disease (IBD) represents an important class of chronic gastrointestinal tract disease. And although there are already several useful treatments to reduce and control the symptoms, there is still no cure. One drug discovery technique used is the computer-aided (in silico) discovery approach which has largely demonstrated efficacy. Computational techniques, when used in combination with traditional drug discovery methodology, greatly increase the chance of drug discovery in a sustainable and economical fashion.

AREAS COVERED

This review aims to provide the most recent and important advances of in silico IBD drug discovery. While this review is mainly focused on QSAR methods, especially those based on molecular topology (MT), additional topics, such as docking or comparative field analysis are also addressed.

EXPERT OPINION

IBD is a worldwide growing health concern that can only be currently treated in symptomatic and palliative way; thus, the search for new drugs is imperative. Computer-aided methods, which focus on the drug-receptor interaction, are essential tool in this regard. It is noted, however that a major problem is that although there are many known receptors associated with IBD, none of these have yet been found essential. The use of other approaches, including QSAR methodology, is certainly a complementary and attractive alternative; especially QSAR methods based on MT, which has proven successful in other drug discovery.

Exploring the binding of BACE-1 inhibitors using comparative binding energy analysis (COMBINE)

BACKGROUND

The inhibition of the activity of β-secretase (BACE-1) is a potentially important approach for the treatment of Alzheimer disease. To explore the mechanism of inhibition, we describe the use of 46 X-ray crystallographic BACE-1/inhibitor complexes to derive quantitative structure-activity relationship (QSAR) models. The inhibitors were aligned by superimposing 46 X-ray crystallographic BACE-1/inhibitor complexes, and gCOMBINE software was used to perform COMparative BINding Energy (COMBINE) analysis on these 46 minimized BACE-1/inhibitor complexes. The major advantage of the COMBINE analysis is that it can quantitatively extract key residues involved in binding the ligand and identify the nature of the interactions between the ligand and receptor.

RESULTS

By considering the contributions of the protein residues to the electrostatic and van der Waals intermolecular interaction energies, two predictive and robust COMBINE models were developed: (i) the 3-PC distance-dependent dielectric constant model (built from a single X-ray crystal structure) with a q2 value of 0.74 and an SDEC value of 0.521; and (ii) the 5-PC sigmoidal electrostatic model (built from the actual complexes present in the Brookhaven Protein Data Bank) with a q2 value of 0.79 and an SDEC value of 0.41.

CONCLUSIONS

These QSAR models and the information describing the inhibition provide useful insights into the design of novel inhibitors via the optimization of the interactions between ligands and those key residues of BACE-1.