Molecular Diversity

A new green mesoporous magnetically heterogeneous catalyst was prepared by the copper immobilization onto magnetic epoxidized soybean oil as a nano bio-support and was utilized for the synthesis of 1,4-disubstituted-1,2,3-triazole derivatives in the presence of amberlite supported azide. A great range of triazole derivatives were synthesized from benzyl halides or epoxides halides in high yields at the room temperature. The catalyst was characterized by various techniques such as FT-IR, XRD, VSM, FE-SEM, EDX, TEM, BET, TGA, and ICP analysis. This catalytic system can be reused for five times without any significant decrease in the catalytic activity. Fe3O4@SiO-ESBO/CuO nanocatalyst and amberlite supported azide as a green catalytic system has been used for the regioselective synthesis of triazole derivatives in water. A large range of triazole derivatives were synthesized from benzyl halides or epoxides in high yields.

We synthesized a series of novel pyrrole- and pyrazole-substituted coumarin derivatives and evaluated their antifungal activity against six phytopathogenic fungi in vitro. The primary assay results demonstrated that some designed compounds displayed potent activities. Among them, compounds 5g, 6a, 6b, 6c, 6d and 6h exhibited more effective control than Osthole against Cucumber anthrax and Alternaria leaf spot. Furthermore, compound 5g displayed stronger antifungal activity against Rhizoctorzia solani (EC50 = 15.4 µg/mL) than positive control Osthole (EC50 = 67.2 µg/mL).

The continuous approval of covalent drugs in recent years for the treatment of diseases has led to an increased search for covalent agents by medicinal chemists and computational scientists worldwide. In the computational parlance, molecular docking which is a popular tool to investigate the interaction of a ligand and a protein target, does not account for the formation of covalent bond, and the increasing application of these conventional programs to covalent targets in early drug discovery practice is a matter of utmost concern. Thus, in this comprehensive review, we sought to educate the docking community about the realization of covalent docking and the existence of suitable programs to make their future virtual-screening events on covalent targets worthwhile and scientifically rational. More interestingly, we went beyond the classical description of the functionality of covalent-docking programs down to selecting the ‘best’ program to consult with during a virtual-screening campaign based on receptor class and covalent warhead chemistry. In addition, we made a highlight on how covalent docking could be achieved using random conventional docking software. And lastly, we raised an alert on the growing erroneous molecular docking practices with covalent targets. Our aim is to guide scientists in the rational docking pursuit when dealing with covalent targets, as this will reduce false-positive results and also increase the reliability of their work for translational research.

In the present study, we report the design and synthesis of novel amide-type hybrid molecules based on anthranilic acid and quinoline or β-carboline heterocyclic scaffolds. Three types of biological screenings were performed: (i) in vitro antiproliferative screening against a panel of solid tumor and leukemia cell lines, (ii) antiviral screening against several RNA viruses, and (iii) anti-quorum sensing screening using gram-negative Chromobacterium violaceum as the reporter strain. Antiproliferative screening revealed a high activity of several compounds. Anthranilamides 12 and 13 with chloroquine core and halogenated anthranilic acid were the most active agents toward diverse cancer cell lines such as glioblastoma, pancreatic adenocarcinoma, colorectal carcinoma, lung carcinoma, acute lymphoblastic, acute myeloid, chronic myeloid leukemia, and non-Hodgkin lymphoma, but also against noncancerous cell lines. Boc-protected analogs 2 and 3 showed moderate activities against the tested cancer cells without toxic effects against noncancerous cells. A nonhalogenated quinoline derivative 10 with N-benzylanthranilic acid residue was equally active as 12 and 13 and selective toward tumor cells. Chloroquine and quinoline anthranilamides 10–13 exerted pronounced antiviral effect against human coronaviruses 229E and OC43, whereas 12 and 13 against coronavirus OC43 (EC50 values in low micromolar range; selectivity indices from 4.6 to > 10.4). Anthranilamides 14 and 16 with PQ core inhibited HIV-1 with EC50 values of 9.3 and 14.1 µM, respectively. Compound 13 displayed significant anti-quorum/biofilm effect against the quorum sensing reporter strain (IC50 of 3.7 μM) with no apparent bactericidal effect.

A simple and practical four-step protocol for the parallel synthesis of 7-heteroaryl-pyrazolo[1,5- $$a$$ ]pyrimidine-3-carboxamides was developed. The synthesis starts with transformation of commercially available 2-acetylpyridine and acetylpyrazine with $$N,$$ $$N$$ -dimethylformamide dimethylacetal into the corresponding $$(E)$$ -3-(dimethylamino)-1-(heteroaryl)prop-2-en-1-ones followed by cyclisation with methyl 5-amino-1 $$H$$ -pyrazole-4-carboxylate to give methyl 7-heteroarylpyrazolo[1,5- $$a$$ ]pyrimidine-3-carboxylates. Hydrolysis of the ester group and subsequent amidation of the so formed carboxylic acids with 12 primary and secondary aliphatic amines furnished a library of 24 title compounds in good overall yields and purity.

Binary and conventional 2D QSAR have been derived for a set of carbonic anhydrase II (CA II) inhibitors. An overall predictive accuracy of 94% was obtained by binary QSAR and of 84% by 2D QSAR model. For both models, preferred molecular descriptor sets were identified, which were overlapping but not identical. Both binary and 2D QSAR captured important molecular features of CA II inhibitors, notably the presence of a sulfonamido group, which is critical for binding, but also hydrophobicity. Promising results were obtained when the derived QSAR models were used to test a set of CA II inhibitors not included in the training set. In binary QSAR, previously unobserved boundary effects were detected both in the analysis of known inhibitors and when screening a large combinatorial library for putative inhibitors. The complementary use of binary and conventional 2D QSAR is thought to increase the accuracy of the lead discovery process by QSAR techniques.

Mucopolysaccharidoses VI (Maroteaux Lamy syndrome) is a metabolic disorder due to the loss of enzyme activity of N-acetyl galactosamine-4-sulphatase arising from mutations in the ARSB gene. The mutated ARSB is the origin for the accumulation of GAGs within the lysosome leading to severe growth deformities, causing lysosomal storage disease. The main focus of this study is to identify the deleterious variants by applying bioinformatics tools to predict the conservation, pathogenicity, stability, and effect of the ARSB variants. We examined 170 missense variants, of which G137V and G144R were the resultant variants predicted detrimental to the progression of the disease. The native along with G137V and G144R structures were fixed as the receptors and subjected to Molecular docking with the small molecule Odiparcil to analyze the binding efficiency and the varied interactions of the receptors towards the drug. The interaction resulted in similar docking scores of − 7.3 kcal/mol indicating effective binding and consistent interactions of the drug with residues CYS117, GLN118, THR182, and GLN517 for native, along with G137V and G144R structures. Molecular Dynamics were conducted to validate the stability and flexibility of the native and variant structures on ligand binding. The overall study indicates that the drug has similar therapeutic towards the native and variant based on the higher binding affinity and also the complexes show stability with an average of 0.2 nm RMS value. This can aid in the future development therapeutics for the Maroteaux Lamy syndrome.

Kinase plays a significant role in various disease signaling pathways. Due to the highly conserved sequence of kinase family members, understanding the selectivity profile of kinase inhibitors remains a priority for drug discovery. Previous methods for kinase selectivity identification use biochemical assays, which are very useful but limited by the protein available. The lack of kinase selectivity can exert benefits but also can cause adverse effects. With the explosion of the dataset for kinase activities, current computational methods can achieve accuracy for large-scale selectivity predictions. Here, we present a multimodal multi-task deep neural network model for kinase selectivity prediction by calculating the fingerprint and physiochemical descriptors. With the multimodal inputs of structure and physiochemical properties information, the multi-task framework could accurately predict the kinome map for selectivity analysis. The proposed model displays better performance for kinase–target prediction based on system evaluations.