A new class of blue light-emitting supramolecular liquid crystalline cone or bowl-shaped compounds were synthesized from substituted 1,3,4-oxadiazoles and 1,3,4-thiadiazoles with calix[4]arene derivatives.

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Abstract The discovery of a drug is known to be quite cumbersome, both in terms of the microscopic fundamental research behind it and the industrial scale manufacturing process. A major concern in drug discovery is the acceleration of the process and cost reduction. The fact that clinical trials cannot be accelerated, therefore, emphasizes the need to accelerate the strategies for identifying lead compounds at an early stage. We, herein, focus on the definition of what would be regarded as a “drug-like” molecule and a “lead-like” one. In particular, “drug-likeness” is referred to as resemblance to existing drugs, whereas “lead-likeness” is characterized by the similarity with structural and physicochemical properties of a “lead”compound, i.e. a reference compound or a starting point for further drug development. It is now well known that a huge proportion of the drug discovery is inspired or derived from natural products (NPs), which have larger complexity as well as size when compared with synthetic compounds. Therefore, similar definitions of “drug-likeness” and “lead-likeness” cannot be applied for the NP-likeness. Rather, there is the dire need to define and explain NP-likeness in regard to chemical structure. An attempt has been made here to give an overview of the general concepts associated with NP discovery, and to provide the foundational basis for defining a molecule as a “drug”, a “lead” or a “natural compound.”

Novel coronavirus SARS-CoV-2 has infected 18 million people with 700,000þ mortalities worldwide and this deadly numeric figure is rapidly rising. With very few success stories, the therapeutic targeting of this epidemic has been mainly attributed to main protease (Mpro), whilst Papain-like proteases (PLpro) also plays a vital role in the processing of replicase polyprotein. Multifunctional roles of PLpro such as viral polypeptide cleavage, de-ISGlyation and immune suppression have made it a promising drug target for therapeutic interventions. Whilst there have been a number of studies and others are ongoing on repurposing and new-small molecule screening, albeit previously FDA approved drugs viz. Chloroquine (CQ) and Hydroxychloroquine (HCQ) have only been found effective against this pandemic. Inspired by this fact, we have carried out molecular docking and dynamics simulation studies of FDA approved CQ and HCQ against SARS-CoV-2 PLpro. The end aim is to characterise the binding mode of CQ and HCQ and identify the key amino acid residues involved in the mechanism of action. Further, molecular dynamics simulations (MDS) were carried out with the docked complex to search for the conformational space and for understanding the integrity of binding mode. We showed that the CQ and HCQ can bind with better binding affinity with PLpro as compared to reference known PLpro inhibitor. Based on the presented findings, it can be anticipated that the SARS-CoV-2 PLpro may act as molecular target of CQ and HCQ, and can be projected for further exploration to design potent inhibitors of SARS-CoV-2 PLpro in the near future.

Novel coronavirus SARS-CoV-2 has infected millions of people with thousands of mortalities globally. The main protease (Mpro) is vital in processing replicase polyproteins. Both the CoV’s Mpro shares 97% identity, with 12 mutations, but none are present in the active site. Although many therapeutics and vaccines are available to combat SARS-CoV-2, these treatments may not be practical due to their high mutational rate. On the other hand, Mpro has a high degree of conservation throughout variants, making Mpro a stout drug target. Here, we report a detailed comparison of both the monomeric Mpro and the biologically active dimeric Mpro using MD simulation to understand the impact of the 12 divergent residues (T35V, A46S, S65N, L86V, R88K, S94A, H134F, K180N, L202V, A267S, T285A and I286L) on the molecular microenvironment and the interaction between crucial residues. The present study concluded that the change in the microenvironment of residues at the entrance (T25, T26, M49 and Q189), near the catalytic site (F140, H163, H164, M165 and H172) and in the substrate-binding site (V35, N65, K88 and N180) is due to 12 mutations in the SARS-CoV-2 Mpro. Furthermore, the involvement of F140, E166 and H172 residues in dimerization stabilizes the Mpro dimer, which should be considered. We anticipate that networks and microenvironment changes identified here might guide repurposing attempts and optimization of new Mpro inhibitors. Supplementary Information The online version contains supplementary material available at 10.1007/s11224-022-02089-6.

Recent advances in the metal‐organic framework (MOF) have accelerated the discovery of novel metal‐based anticancer, antibacterial and antimalarial compounds. This is substantiated by many serendipitously discovered metals (Ru, Rh, and Ir) based inhibitors that established the importance of metal inserted into the known organic scaffold. Conversely, it is possible to design novel bioactive compounds by mimicking hypervalent carbon atoms by transition metals. This process can be facilitated by computational drug discovery by treating metal centres using optimized parameters that can be used for molecular docking and molecular dynamics simulations. Further, the method can be plugged with high computational power and refined algorithms to interpret chemical phenomena with atomic‐level insights. In the present work, we have demonstrated an approach for parameterizing three organometallic ligands (FLL, E52, and staurosporine) using MCPB.py. In particular, we report that E52 and FLL have a better shape complimentary and affinity compared to staurosporine identified inhibitor (staurosporine) against Calcium‐dependent protein kinases 2 (CDPK2). This study also revealed that a flexible approach (ensemble) outperforms the given target with dynamic movements. The calculated MM‐PBSA energies for staurosporine, FLL and E52 were −66.461±2.192, −67.182±1.971 and −91.339±2.745 kJ/mol, respectively.

In the present work, multiple pharmacophore-based virtual screening of the SPECS natural product database was carried out to identify novel inhibitors of the validated biological target, InhA. The pharmacophore models were built from the five different groups of the co-crystallized ligands present within the active site. The generated models with the same features from each group were pooled and subjected to the test set validation, receiver-operator characteristic analysis and Güner-Henry studies. A set of five hypotheses with sensitivity > 0.5, specificity > 0.5, area under curve (AUC) > 0.7, and goodness of hit score > 0.7 were retrieved and exploited for the virtual screening. The common hits (87 molecules) obtained from these hypotheses were processed via drug-likeness filters. The filtered molecules (27 molecules) were compared for the binding modes and the top scored molecules (12 molecules) along with the reference (triclosan (TCL), docking score = -11.65 kcal/mol) were rescored and reprioritized via molecular mechanics-generalized Born surface area approach. Eventually, the stability of reprioritized (10 molecules) docked complexes was scrutinized via molecular dynamics simulations. Moreover, the quantum chemical studies of the dynamically stable compounds (9 molecules) were performed to understand structural features essential for the activity. Overall, the protocol resulted in the recognition of nine lead compounds that can be targeted against InhA.