A facile and single pot strategy for the synthesis of novel naphthyridine derivatives under microwave irradiation conditions using ZnCl2 as catalyst, evaluation of AChE inhibitory activity, and molecular modeling studies

1,2,3-Triazolo[4,5-b]aminoquinolines: Design, synthesis, structure, acetylcholinesterase (AChE) and butyrylcholinesterase (BChE) inhibitory activity, and molecular docking of novel modified tacrines

An efficient [4 + 2] cyclization protocol to synthesize a series of twelve examples of 1,2,3-triazolo[4,5-b]aminoquinolines (5) as novel structurally modified tacrines was obtained by reacting readily accessible precursors (i.e., 3-alky(aryl)-5-amino-1,2,3-triazole-4-carbonitriles (3)) and selected cycloalkanones (4) of five-, six-, and seven-membered rings. We evaluated the AChE and BChE inhibitory activity of the novel modified tacrines 5, and the compound derivatives from cyclohexanone (4b) showed the best AChE and BChE inhibitory activities. Specifically, 1,2,3-triazolo[4,5-b]aminoquinolines 5bb obtained from 3-methyl-carbonitrile (3b) showed the highest AChE (IC50 = 12.01 μM), while 5ib from 3-sulfonamido-carbonitrile (3i) was the most significant inhibitor for BChE (IC50 = 1.78 μM). In general, the inhibitory potency of compound 5 was weaker than the pure tacrine reference, and our findings may help to design and develop novel anticholinesterase drugs based on modified tacrines.

Structure-based drug repurposing: traditional and advanced AI/ML-aided methods

The current global health emergency in the form of the Coronavirus 2019 (COVID-19) pandemic has highlighted the need for fast, accurate, and efficient drug discovery pipelines. Traditional drug discovery projects relying on in vitro high-throughput screening (HTS) involve large investments and sophisticated experimental set-ups, affordable only to big biopharmaceutical companies. In this scenario, application of efficient state-of-the-art computational methods and modern artificial intelligence (AI)-based algorithms for rapid screening of repurposable chemical space [approved drugs and natural products (NPs) with proven pharmacokinetic profiles] to identify the initial leads is a powerful option to save resources and time. Structure-based drug repurposing is a popular in silico repurposing approach. In this review, we discuss traditional and modern AI-based computational methods and tools applied at various stages for structure-based drug discovery (SBDD) pipelines. Additionally, we highlight the role of generative models in generating molecules with scaffolds from repurposable chemical space. Teaser: This review highlights the importance of repurposable chemical space, and the contributions of conventional in silico approaches and modern machine-learning algorithms for rapid structure-based drug repurposing.

A Four-Component Domino Reaction: An Eco-Compatible and Highly Efficient Construction of 1,8-Naphthyridine Derivatives, Their In Silico Molecular Docking, Drug Likeness, ADME, and Toxicity Studies

A multicomponent domino reaction of enaminone, malononitrile, and o-phthalaldehyde has been established, providing direct access to novel highly functionalized pentacyclic cyclopenta [b] indeno [1, 2, 3-de] [1,8] naphthyridine derivatives. The simplicity of execution, readily available substrates, high yields, excellent functional group tolerance, scalability, and good scores of environmental parameters make this synthetic strategy more sustainable and worthy of further attention. This one-pot transformation, which involved multiple steps and did not require the use of a catalyst, constructed four new C-C bonds, two new C-N bonds, and three new rings, with efficient use of all reactants. Furthermore, we performed in silico molecular docking analysis for prediction of anticancer (against human topoisomerase IIβ protein) and antimicrobial (against E.coli. DNA gyrase B protein) activities. Drug likeness and ADMET studies were also predicted. Overall investigation indicates that compound 6i may serve as a candidate that could be developed as potential anticancer and antimicrobial agent among all.

Hybrid Dynamic Pharmacophore Models as Effective Tools to Identify Novel Chemotypes for Anti-TB Inhibitor Design: A Case Study With Mtb-DapB

Antimicrobial resistance (AMR) is one of the most serious global public health threats as it compromises the successful treatment of deadly infectious diseases like tuberculosis. New therapeutics are constantly needed but it takes a long time and is expensive to explore new biochemical space. One way to address this issue is to repurpose the validated targets and identify novel chemotypes that can simultaneously bind to multiple binding pockets of these targets as a new lead generation strategy. This study reports such a strategy, dynamic hybrid pharmacophore model (DHPM), which represents the combined interaction features of different binding pockets contrary to the conventional approaches, where pharmacophore models are generated from single binding sites. We have considered Mtb-DapB, a validated mycobacterial drug target, as our model system to explore the effectiveness of DHPMs to screen novel unexplored compounds. Mtb-DapB has a cofactor binding site (CBS) and an adjacent substrate binding site (SBS). Four different model systems of Mtb-DapB were designed where, either NADPH/NADH occupies CBS in presence/absence of an inhibitor 2, 6-PDC in the adjacent SBS. Two more model systems were designed, where 2, 6-PDC was linked to NADPH and NADH to form hybrid molecules. The six model systems were subjected to 200 ns molecular dynamics simulations and trajectories were analyzed to identify stable ligand-receptor interaction features. Based on these interactions, conventional pharmacophore models (CPM) were generated from the individual binding sites while DHPMs were created from hybrid-molecules occupying both binding sites. A huge library of 1,563,764 publicly available molecules were screened by CPMs and DHPMs. The screened hits obtained from both types of models were compared based on their Hashed binary molecular fingerprints and 4-point pharmacophore fingerprints using Tanimoto, Cosine, Dice and Tversky similarity matrices. Molecules screened by DHPM exhibited significant structural diversity, better binding strength and drug like properties as compared to the compounds screened by CPMs indicating the efficiency of DHPM to explore new chemical space for anti-TB drug discovery. The idea of DHPM can be applied for a wide range of mycobacterial or other pathogen targets to venture into unexplored chemical space.