Ligand-based pharmacophore filtering, atom based 3D-QSAR, virtual screening and ADME studies for the discovery of potential ck2 inhibitors

Discovery of newer pyrazole derivatives with potential anti-tubercular activity via 3D-QSAR based pharmacophore modelling, virtual screening, molecular docking and molecular dynamics simulation studies

Tuberculosis is one of the leading causes of death of at least one million people annually. The deadliest infectious disease has caused more than 120 million deaths in humans since 1882. The cell wall structure of Mycobacterium tuberculosis is important for survival in the host environment. InhA is the foremost target for the development of novel anti-tubercular agents. Therefore, we report pharmacophore-based virtual screening (ZINC and ASINEX databases) and molecular docking study (PDB Code: 4TZK) to identify and design potent inhibitors targeting to InhA. A five-point pharmacophore model AADHR_1 (with R2 = 0.97 and Q2 = 0.77) was developed by using 47 compounds with its reported MIC values. Further, to identify and design potent hit molecules based on lead identification and modification, generated hypothesis employed for virtual screening using ZINC and ASINEX databases. Predicted pyrazole derivatives further gauged for drug likeliness and docked against enoyl acyl carrier protein reductase to categorize the essential amino acid interactions to the active site of the enzyme. Structure elucidation of these synthesized compounds was carried out using IR, MS, 1H-NMR and 13C-NMR spectroscopy. Amongst all the synthesized compounds, some of the compounds 5a, 5c, 5d and 5e were found to be potent with their MIC ranging from 2.23 to 4.61 µM. Based on preliminary anti-tubercular activity synthesized potent molecules were further assessed for MDR-TB, XDR-TB and cytotoxic study.

Advances in the discovery of new chemotypes through ultra-large library docking

INTRODUCTION

The size and complexity of virtual screening libraries in drug discovery have skyrocketed in recent years, reaching up to multiple billions of accessible compounds. However, virtual screening of such ultra-large libraries poses several challenges associated with preparing the libraries, sampling, and pre-selection of suitable compounds. The utilization of artificial intelligence (AI)-assisted screening approaches, such as deep learning, poses a promising countermeasure to deal with this rapidly expanding chemical space. For example, various AI-driven methods were recently successfully used to identify novel small molecule inhibitors of the SARS-CoV-2 main protease (M^pro).

AREAS COVERED

This review focuses on presenting various kinds of virtual screening methods suitable for dealing with ultra-large libraries. Challenges associated with these computational methodologies are discussed, and recent advances are highlighted in the example of the discovery of novel M^pro inhibitors targeting the SARS-CoV-2 virus.

EXPERT OPINION

With the rapid expansion of the virtual chemical space, the methodologies for docking and screening such quantities of molecules need to keep pace. Employment of AI-driven screening compounds has already been shown to be effective in a range from a few thousand to multiple billion compounds, furthered by de novo generation of drug-like molecules without human interference.

3D QSAR based Virtual Screening of Flavonoids as Acetylcholinesterase Inhibitors

In an attempt to develop therapeutic agents to treat Alzheimer's disease, a series of flavonoid analogues were collected, which already had established acetylcholinesterase (AChE) enzyme inhibition activity. For each molecule we also collected biological activity data (Ki). Then, 3D-QSAR (quantitative structure-activity relationship model) was developed which showed acceptable predictive and descriptive capability as represented by standard statistical parameters r2 and q2. This SAR data can explain the key descriptors which can be related to AChE inhibitory activity. Using the QSAR model, pharmacophores were developed based on which, virtual screening was done and a dataset was obtained which loaded as a prediction set to fit the developed QSAR model. Top 10 compounds fitting the QSAR model were subjected to molecular docking. CHEMBL1718051 was found to be the lead compound. This study is offering an example of a computationally-driven tool for prioritisation and discovery of probable AChE inhibitors. Further, in vivo and in vitro testing will show its therapeutic potential.

A holistic molecular modelling approach to design novel indole-2-carboxamide derivatives as potential inhibitors of MmpL3

Tuberculosis is an infectious air-borne disease and one of the leading causes of death globally among all infectious diseases. There is an urgent need to develop antitubercular drugs that would be highly efficient and less toxic than the presently available marketed drugs. Mycobacterium membrane protein large 3 (MmpL3) is an emerging drug target in tuberculosis with various classes of molecules that have been known to inhibit it. In this study, a dataset of indole-2-carboxamides showing antitubercular activity by inhibiting MmpL3 was utilized. Initially, a chimera-based homology model was developed and docking was performed with the filtered dataset to analyse the interactions. Thereafter, molecular dynamics simulations were run with representative molecules to gain a better insight on the binding patterns. To attain a more quantitative correlation, an atom-based 3D QSAR model was developed which complemented the results from the previous models. A library of novel indole-2-carboxamides was then generated using core hopping-based ligand enumeration and upon screening on our workflow model it predicted three molecules as potent antitubercular compounds. This work not only helps to gain new insights on the interactions at the MmpL3 binding site but also provides novel indole-2-carboxamides having the potential to become antitubercular drugs in future.

Discovery, biological evaluation and molecular dynamic simulations of butyrylcholinesterase inhibitors through structure-based pharmacophore virtual screening

Aim: Butyrylcholinesterase (BChE) is a crucial therapeutic target because it is associated with multiple pathological elements of Alzheimer's disease (AD). An integrated computational strategy was employed to exploit effective BChE inhibitors. Methods & results: Ten compounds derived from the Enamine database by structure-based pharmacophore virtual screening were further evaluated for biological activity; out of the ten, only five had an IC₅₀ of less than 100 μM. Among these five compounds, a new molecule, 970180, presented the most potency against BChE, with an IC₅₀ of 4.24 ± 0.16 μM, and acted as a mixed-type inhibitor. Molecular dynamic simulations and absorption, distribution, metabolism and excretion prediction further confirmed its high potential as a good candidate of BChE inhibitor. Furthermore, cytotoxicity of molecule 970180 was not observed at concentrations up to 50 μM, and the molecule also showed a prominent neuroprotective effect compared with tacrine at 25 and 50 μM. Conclusion: This study provides an effective structure-based pharmacophore virtual screening method to discover BChE inhibitors and provide new choices for the development of BChE inhibitors, which may be beneficial for AD patients.

Computational Approaches in Preclinical Studies on Drug Discovery and Development

Because undesirable pharmacokinetics and toxicity are significant reasons for the failure of drug development in the costly late stage, it has been widely recognized that drug ADMET properties should be considered as early as possible to reduce failure rates in the clinical phase of drug discovery. Concurrently, drug recalls have become increasingly common in recent years, prompting pharmaceutical companies to increase attention toward the safety evaluation of preclinical drugs. In vitro and in vivo drug evaluation techniques are currently more mature in preclinical applications, but these technologies are costly. In recent years, with the rapid development of computer science, in silico technology has been widely used to evaluate the relevant properties of drugs in the preclinical stage and has produced many software programs and in silico models, further promoting the study of ADMET in vitro. In this review, we first introduce the two ADMET prediction categories (molecular modeling and data modeling). Then, we perform a systematic classification and description of the databases and software commonly used for ADMET prediction. We focus on some widely studied ADMT properties as well as PBPK simulation, and we list some applications that are related to the prediction categories and web tools. Finally, we discuss challenges and limitations in the preclinical area and propose some suggestions and prospects for the future.