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Abstract: The synthesis of organofluorine compounds plays a vital role in the field of pharmaceuticals, agrochemicals, and materials since the fluorine-containing compounds have shown improved biological and physicochemical properties. Significant research has been directed towards synthesizing organofluorine compounds because organofluorine compounds have been displayed in several biological activities, including anticancer, antitumor, antihypertensive, antidepressant, anti-HIV and treatment of stroke. Due to several pharmaceutical applications of organofluorine compounds, numerous conventional as well as green synthetic methodologies have been developed for the synthesis of fluorine-containing compounds. However, particularly, the synthesis of organofluorine compounds using green approaches has been continuously attracting research interest since green approaches have several advantages, including the use of inexpensive and nontoxic reagents, catalytic, highly efficient, short reaction time, energy efficient, high reaction yields, and environmentally benign over the conventional methods. Among the green tools, the use of microwave-assisted synthesis, water, and ionic liquids as green solvent/reaction media, organocatalysts, photocatalysts, and solvent-free and catalyst-free reaction conditions have been exploited in the past decades for the synthesis of organofluorine compounds. In this review, we highlighted the recent developments in various green methods for the synthesis of organofluorine compounds via electrophilic fluorination for synthesis of various fluorohydrins, fluorinated acyclic & cyclic β-ketoesters, 1,3-dicarbonyl compounds, cyclic and acyclic ketones, α-cyanoesters, α-aryl-tetralones, α-amino acids, flavanones, and several fluorinated heterocycles such as fluorinated pyridine, pyrimidine, pyrrole, pyrazolone, benzofuran, indole, flavanone, and coumarin derivatives. In addition, some green methodologies have been highlighted for the synthesis of biologically active fluorinated compounds, including HIV-1 integrase inhibitors, 20-deoxy-20-fluorocamptothecin, fluorinated estrone, sclareolide, BMS-204352 (MaxiPost), fluorinated naproxen and fluoxetine.

Urea transporter is a membrane transport protein. It is involved in the transferring of urea across the cell membrane in humans. Along with urea transporter A, urea transporter B (UT-B) is also responsible for the management of urea concentration and blood pressure of human. The inhibitors of urea transporters have already generated a huge attention to be developed as alternate safe class of diuretic. Unlike conventional diuretics, these inhibitors are suitable for long-term therapy without hampering the precious electrolyte imbalance in the human body. In this study, UT-B inhibitors were analysed by using multi-chemometric modelling approaches. The possible pharmacophore features along with favourable and unfavourable sub-structural fingerprints for UT-B inhibition are extracted. This information will guide the medicinal chemist to design potent UT-B inhibitors in future.

Phosphodiesterase 5 (PDE5) falls under a broad category of metallohydrolase enzymes responsible for the catalysis of the phosphodiesterase bond, and thus it can terminate the action of cyclic guanosine monophosphate (cGMP). Overexpression of this enzyme leads to development of a number of pathological conditions. Thus, targeting the enzyme to develop inhibitors could be useful for the treatment of erectile dysfunction as well as pulmonary hypertension. In the current study, several molecular modelling techniques were utilized including Bayesian classification, single tree and forest tree recursive partitioning, and genetic function approximation to identify crucial structural fingerprints important for optimization of tri-substituted pyrazoline derivatives as PDE5 inhibitors. Later, various machine learning models were also developed that could be utilized to predict and screen PDE5 inhibitors in the future.

Myeloid cell leukemia (Mcl-1) protein, belonging to a large BCL2 family of proteins, is a significant target towards cancer chemotherapy. The over-expression of this protein is also responsible for the development and progression of severe multi-drug resistance in cancer patients. This present study has focused the attention towards unmasking of important structural fingerprints for promoting or hindering Mcl-1 inhibitory activity of some indole-based derivatives by using Monte Carlo optimization-based QSAR approach. Twenty-one robust classification models were generated. The best model (by using SMILES and HSG-based descriptors) was selected, and several important good and bad structural fingerprints for Mcl-1 inhibition were identified. Some of the fingerprints were matching with the earlier study (New J. Chem., 2020, 44, 17494-17506) and some new fingerprints were also generated. The modelling study will help the researchers in the lead optimization of some indole-based Mcl-1 inhibitors in the future.

Novel coronavirus disease 2019 (COVID-19) emerges as a serious threat to public health globally. The rapid spreading of COVID-19, caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2), proclaimed the multitude of applied research needed not only to save the human health but also for the environmental safety. As per the recent World Health Organization reports, the novel corona virus may never be wiped out completely from the world. In this connection, the inhibitors already designed against different targets of previous human coronavirus (HCoV) infections will be a great starting point for further optimization. Pinpointing biochemical events censorious to the HCoV lifecycle has provided two proteases: a papain-like protease (PLpro) and a 3C-like protease (3CLpro) enzyme essential for viral replication. In this study, naphthyl derivatives inhibiting PLpro enzyme were subjected to robust molecular modelling approaches to understand different structural fingerprints important for the inhibition. Here, we cover two main aspects such as (a) exploration of naphthyl derivatives by classification QSAR analyses to find important fingerprints that module the SARS-CoV PLpro inhibition and (b) implications of naphthyl derivatives against SARS-CoV-2 PLpro enzyme through detailed ligand–receptor interaction analysis. The modelling insights will help in the speedy design of potent broad spectrum PLpro inhibitors against infectious SARS-CoV and SARS-CoV-2 in the future. Graphical Abstract Supplementary Information The online version contains supplementary material available at(10.1007/s11030-021-10198-3) .

Main protease (Mpro) of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) intervenes in the replication and transcription processes of the virus. Hence, it is a lucrative target for anti-viral drug development. In this study, molecular modeling analyses were performed on the structure activity data of recently reported diverse SARS-CoV-2 Mpro inhibitors to understand the structural requirements for higher inhibitory activity. The classification-based quantitative structure–activity relationship (QSAR) models were generated between SARS-CoV-2 Mpro inhibitory activities and different descriptors. Identification of structural fingerprints to increase or decrease in the inhibitory activity was mapped for possible inclusion/exclusion of these fingerprints in the lead optimization process. Challenges in ADME properties of protease inhibitors were also discussed to overcome the problems of oral bioavailability. Further, depending on the modeling results, we have proposed novel as well as potent SARS-CoV-2 Mpro inhibitors. Graphic Abstract Electronic supplementary material The online version of this article (10.1007/s11030-020-10166-3) contains supplementary material, which is available to authorized users.

Indole moiety is often found in different classes of pharmaceutically active molecules having various biological activities including anticancer, anti-viral, anti-psychotic, antihypertensive, anti-migraine, anti-arthritis and analgesic activities. Due to enormous applications of indole derivatives in pharmaceutical chemistry, a number of conventional synthetic methods as well as green methodology have been developed for their synthesis. Green methodology has many advantages including high yields, short reaction time, and inexpensive reagents, highly efficient and environmentally benign over conventional methods. Currently, the researchers in academia as well as in pharmaceutical industries have been developing various methods for the chemical synthesis of indole based compounds via green approaches to overcome the drawbacks of conventional methods. This review reflects the last ten years developments of the various greener methods for the synthesis of indole derivatives by using microwave, ionic liquids, water, ultrasound, nanocatalyst, green catalyst, multicomponent reaction and solvent-free reactions etc. (please see the scheme below). Furthermore, the applications of green chemistry towards developments of indole containing pharmaceuticals and their biological studies have been represented in this review.

The human breast cancer resistance protein (BCRP), one of the members of the large ATP binding cassette (ABC) transporter superfamily, is crucial for resistance against chemotherapeutic agents. Currently, it has been emerged as one of the best biological targets for the designing of small molecule drugs capable of eliminating multidrug resistance in breast cancer. In order to gain insights into the relationship between the molecular structure of compounds and the ABCG2 inhibition, a multi-QSAR approach using different methods was performed on a dataset of 294 ABCG2 inhibitors with diverse scaffolds. The best models obtained by different chemometric methods have the following statistical characteristics: Monte Carlo Optimization-based QSAR (sensitivity = 0.905, specificity = 0.6255, accuracy = 0.756, and MCC = 0.545), Bayesian classification model (sensitivity = 0.735, specificity = 0.775, and concordance = 0.757); structural and physicochemical interpretation analysis-random forest method (balance accuracy = 0.750, sensitivity = 0.810, and specificity = 0.700). Additionally, structural fingerprints modulating the ABCG2 inhibitory properties were identified from the best models of each method and also validated with each other. The current modelling study is an attempt to get a deep insight into the different important structural fingerprints modulating ABCG2 inhibition.

Monte Carlo based method by using either SMILES based or combination of SMILES and Graph-based descriptors is an important strategy to build the QSAR/QSTR model for prediction of different biologic...

Structural characteristics of clocortolone pivalate are unique in the topical corticosteroid field having high penetration power through the stratum corneum of skin as well as low corticosteroid-related adverse effects. The molecule was thoroughly studied by 13C 2DPASS CP MAS NMR and spin–lattice relaxation time measurements. Molecular correlation time at different carbon nuclei positions was calculated by assuming that the chemical shift anisotropy interaction and heteronuclear dipole–dipole interaction play vital roles in the 13C spin–lattice relaxation mechanism. The CSA parameters are substantially varied at different carbon nuclei sites. This suggests that the electronic distribution surrounding the carbon nuclei varies widely when the same carbon atom is placed in different chemical surroundings of the molecule. The spinning CSA sideband pattern for C11, C17, C26 nuclei is axially symmetric. The asymmetry parameter is very small (≤0.3) for C2, C5, C10, C22, C23, C24 nuclei, and it is reasonably high (≥0.9) for C3, C4, C6, C18, C19, C21 nuclei. The anisotropy parameter is very high for double bonded C14, C15, C18, C19, C21, C22, and C23 nuclei. Spin–lattice relaxation time and molecular correlation time are also varied substantially for carbon nuclei situated at various positions of the molecule. The spin–lattice relaxation time is slow for carbon nuclei residing at the carbon ring, and it is very fast for C12, C17, C16, C26 carbon nuclei situated at the side portion of the molecule. Molecular correlation time is of the order of 10−4 s for those carbon nuclei attached with neighbouring carbon or oxygen atoms by double bonds like C14, C15, C18, C19, C21, C22, and C23. It implies that the molecular correlation time is very high for those carbon nuclei associated with high values of the chemical shift anisotropy parameter. In contrast, the molecular correlation time is of the order of 10−8 s for C12, C16, and C17 carbon nuclei. From these studies, it is clear that the various portions of the molecule exhibit different degrees of motion and the dynamics is related with the structural characteristics of the molecule. These investigations on important drug clocortolone pivalate by solid state NMR will help researchers to understand the structure and dynamics of the molecule, which will give a direction to develop advance corticosteroids.

In humans, the over-expression of Mcl-1 protein causes different cancers and it is also responsible for cancer resistance to different cytotoxic agents. Thus, discovery of potential inhibitors of Mcl-1 is very important and both the pharmaceutical industry and academia are looking at it in the quest for new anticancer drugs. In the present study, different molecular modelling techniques such as recursive partitioning, Bayesian classification, structural and physico-chemical interpretation analysis and pharmacophore mapping were employed in order to identify the crucial structural fingerprints important for the optimization of 143 indole derivatives as Mcl-1 inhibitors. These modelling studies emphasize that hydrophobic naphthyl rings, methyl-substituted 1H-pyrazole moiety, N(1)-tethered morpholinoethyl group, chloro substitutions at the 6th position of indole nucleus etc. are beneficial for Mcl-1 binding. Finally, statistically validated classical QSAR and machine learning-based models were also developed for screening and prediction of different indole derivatives as Mcl-1 inhibitors. The modelling analyses will help medicinal chemists to design potent Mcl-1 inhibitors in future. Thus, the present study was an attempt to speed up the anticancer drug discovery of indole-based Mcl-1 inhibitors.

There is a great demand to utilize different in silico tools to address unwanted drug metabolism and pharmacokinetics issues in drug discovery. There is also a demand to optimize existing drug therapeutics by understanding their interactions with various transporters in the body. The cation membrane transporter is among one of the major crucial transporters within the body to regulate movement of foreign molecules/drugs across the cell membrane. The prime objective of this study is to find out the structural fingerprints within molecules to be recognized as substrate/non-substrate against human organic cation transporters (hOCTs). This study may pave the way for a more detailed understanding of the physiological and pharmacological roles of transporters as well as in predicting pharmacokinetics and pharmacodynamics in the design and development of better cationic drugs. The in silico study involving physicochemical parameter analysis revealed a trend that was distinct for substrate and non-substrate molecules present in the dataset. A hyperfine geometric distribution based strategy was also utilized for obtaining the detailed distribution of different functional moiety in substrates and non-substrates for hOCTs. Classification QSAR study by Monte-Carlo optimization and Bayesian modelling methods were used for the identification of crucial structural fingerprints important for substrate activity. Classification QSAR study with the help of Monte-Carlo optimization in case of hOCT1 revealed important structural attributes like aromatic rings with branching, two aromatic rings at one bond distance, presence of sulphur atom in the compound, etc. These seemed to have vital roles of hOCT1 substrate activity. Similarly, a Bayesian classification model was also generated which gave important fingerprints for the analysis of substrates in the case of hOCT1. Models built for hOCT2 by Monte Carlo optimization and Bayesian classification study have not given significant results from the dataset. Finally, various machine learning methods (kNN, ASNN, WEKA RF, XG-BOOST) with the combination of various descriptors were utilized for the development of models and consensus models were generated separately using the best models for both hOCT1 and hOCT2. The consensus models were utilized to predict some recently FDA approved drugs for possibly hOCT1 and hOCT2 substrates. To confirm further, the predicted compounds were docked and the analysis showed that they may bind to the different binding sites of the transporter.

The structure and dynamics of itraconazole were investigated by 13C 2DPASS MAS SSNMR and spin-lattice relaxation time measurement to get an insight into its multiple biological activities, e.g., antifungal, antiviral, anticancer activities, etc. The molecular correlation time at chemically different sites of carbon nuclei was calculated by considering that the spin-lattice relaxation mechanism is mainly dominated by chemical shift anisotropy interaction and heteronuclear dipole–dipole interaction. The spin-lattice relaxation time is long for C35, C6, C5, and C34 carbon nuclei that participated in the 1, 2, 4-triazole ring. On the contrary, it is comparatively shorter for C1, C2, C3, and C4 carbon nuclei associated with the sec-butyl group in the triazolane side-chain region. Chemical shift anisotropy (CSA) parameters of C5, C6, C34, and C35 nuclei are much higher than those of C1, C2, C3, C4 nuclei, indicating that the relaxation mechanism at a high value of magnetic field is predominated by chemical shift anisotropy interaction. The molecular correlation time of carbon nuclei residing at the side-chain region is 2–3 orders of magnitude lesser than that of those participated in the 1,2,4-triazole ring. The spin-lattice relaxation time is very long for carbon nuclei C28 and C30 bonded with chlorine. Asymmetry and anisotropy parameters are also very high for the spinning CSA sideband pattern corresponding to the C28 and C30 nuclei. The molecular correlation time is on the order of 10–3 s for C28 and 10–4 s for C30, whereas for side-chain carbon nuclei, it is on the order of 10–6 s. This suggests that the effective magnetic field experienced by C28 and C30 nuclei is affected by the polarization of the chemical bond. A huge variation in molecular correlation time is observed for chemically different sites of carbon nuclei of the itraconazole molecule. These investigations vividly portrayed how the structure is correlated with the dynamics of a valuable drug, itraconazole, with multiple biological activities. This study will enlighten the way of inventing advance medicine for multiple biological activities in the pharmaceutical industry.

Kinases are considered as important signalling enzymes that illustrate 20% of the druggable genome. Human kinase family comprises more than 500 protein kinases and about 20 lipid kinases. Protein kinases are responsible for the mechanism of protein phosphorylation. These are necessary for regulation of various cellular activities including proliferation, cell cycle, apoptosis, motility, growth, differentiation, etc. Their deregulation leads to disruption of many cellular processes leading to different diseases most importantly cancer. Thus, kinases are considered as valuable targets in different types of cancer as well as other diseases. Researchers around the world are actively engaged in developing inhibitors based on different chemical scaffolds. Indole represents as a versatile scaffold in the naturally occurring and bioactive molecules. It is also used as a privileged scaffold for the target-based drug design against different diseases. This present article aim to review the applications of indole scaffold in the design of inhibitors against different tyrosine kinases such as epidermal growth factor receptors (EGFRs), vascular endothelial growth factor receptors (VEGFRs), platelet-derived growth factor receptor (PDGFRs), etc. Important structure activity relationships (SARs) of different indole derivatives were discussed. The present work is an attempt to summarize all the crucial structural information which is essential for the development of indole based tyrosine kinase inhibitors with improved potency.

Cancer is a rapidly growing disease in modern society. Chemotherapy is the first choice for cancer treatment. Design and development of new chemotherapeutic drugs by targeting specific proteins are put down by a high attrition rate at different stages. Fragment-based drug design (FBDD) is one of the successful structure-based drug design processes to avoid attrition-related problems. This review highlighted the computational and experimental FBDD techniques used to design molecules with anticancer properties. This study describes FBBD strategies for different targets like aurora kinase, phosphoinositide-dependent protein kinase-1 (PDK1), signal transducer and activator of transcription 3 (STAT3), myeloid cell leukemia-1 (Mcl-1), tankyrase (TNKS), choline kinase, protein kinase, tyrosine kinase and lysine-specific demethylase 1 (LSD1) which are vital targets for cancer treatments. This review will enrich the scientific community to understand the fragment-based design strategies for finding suitable leads over high throughput screening (HTS) in the future.

The protease enzyme, matrix metalloproteinase-2 (MMP-2) has been a target of choice for the drug development due to its multi-façade involvement in numerous diseased conditions including cancer. To find a selective MMP-2 inhibitor several computational strategies are employed in its design and discovery. In these strategies, protein structure of MMP-2 is an inevitable part to formulate effective structure-based drug design (SBDD) of selective MMP-2 inhibitors. In the present communication, several crystal structures of MMP-2 have been analyzed with different statistical parameters and their implementations in SBDD of inhibitors are scrutinized. In addition, binding mode analyses of various classes of inhibitors are discussed to pinpoint the effective design of selective inhibitors by maximizing its interaction with the MMP-2 enzyme binding site. This may provide a crucial insight for exploring the numerous possibilities for SBDD of MMP-2 inhibitors to accelerate anticancer drug discovery efforts.

Hypertension is a major concern for a wide array of patients. The traditional drugs are commonly referred as ‘water pills’ and these molecules have been successful in alleviating hypertension. However, this comes at the high expense of precious electrolytes in our body. To dissipate this major adverse effect, the urea transporter inhibitors play especially important roles in maintaining the fluid balance by maintaining the concentration of urea in the inner medullary collecting duct. The purpose of this communication is to provide insights into the structural feature of these target proteins and inhibition of both urea transporter types A (UT-A) and B (UT-B) selectively and non-selectively with a special focus on the UT-A inhibitors as they are the primary target for diuresis. It was observed that a wide class of drugs such as thiourea analogues, 2,7-disubstituted fluorenones can inhibit both the protein non-selectively whereas 8-hydroxyquinoline, aminothiazolone, 1,3,5-triazine, triazolothienopyrimidine, thienoquinoline, arylthiazole, γ-sultambenzosulfonamide and 1,2,4-triazoloquinoxaline classes of compounds inhibit UT-A. The goal of this study is to highlight the important aspects that may be useful to understanding the perspectives of urea transporter inhibitors in rational drug discovery.

Indole derivatives have attractive anticancer properties and may be a future hope for better anticancer drug(s) of low toxicity and high potency. In this paper, syntheses of 2-phenylindole derivati...

Indoleamine-2,3-dioxygenase 1 (IDO1) is an extrahepatic, heme-containing and tryptophan-catalyzing enzyme responsible for causing blockade of T-cell proliferation and differentiation by depleting tryptophan level in cancerous cells. Therefore, inhibition of IDO1 may be a useful strategy for immunotherapy against cancer. In this study, 448 structurally diverse IDO1 inhibitors with a wide range of activity has been taken into consideration for classification QSAR analysis through Monte Carlo Optimization by using different splits as well as different combinations of SMILES-based, graph-based and hybrid descriptors. The best model from Monte Carlo optimization was interpreted to find out the good and bad structural fingerprints for IDO1 and further justified by using Bayesian classification QSAR modeling. Among the three splits in Monte Carlo optimization, the statistics of the best model was obtained from Split 3: sensitivity = 0.87, specificity = 0.91, accuracy = 0.89 and MCC = 0.78. In Bayesian classification modeling, the ROC scores for training and test set were found to be 0.91 and 0.86, respectively. The combined modeling analysis revealed that the presence of aryl hydrazyl sulphonyl moiety, furazan ring, halogen substitution, nitro group and hetero atoms in aromatic system can be very useful in designing IDO1 inhibitors. All the good and bad structural fingerprints for IDO1 were identified and are justified by correlating these fragments to the inhibition of IDO1 enzyme. These structural fingerprints will guide the researchers in this field to design better inhibitors against IDO1 enzyme for cancer immunotherapy. Communicated by Ramaswamy H. Sarma

Camptothecin (CPT), obtained from Camptotheca acuminata (Nyssaceae), is a quinoline type of alkaloid. Apart from various traditional uses, it is mainly used as a potential cytotoxic agent acting against a variety of cancer cell lines. Though searches have been continued for last six decades, still it is a demanding task to design potent and cytotoxic CPTs. Different CPT analogs are synthesized to enhance the cytotoxic potential as well as to increase the pharmacokinetic properties of these analogs. Some of these analogs were proven to be clinically effective in different cancer cell lines. In this article, different CPT analogs have been highlighted extensively to get a detail insight about the structure-property relationships as well as different quantitative structure-activity relationships (QSARs) modeling of these analogs are also discussed. This study may be beneficial for designing newer CPT analogs in future.

HRV 3 C protease (HRV 3Cpro) is an important target for common cold and upper respiratory tract infection. Keeping in view of the non-availability of drug for the treatment, newer computer-based modelling strategies should be applied to rationalize the process of antiviral drug discovery in order to decrease the valuable time and huge expenditure of the process. The present work demonstrates a structure wise optimization using Monte Carlo-based QSAR method that decomposes ligand compounds (in SMILES format) into several molecular fingerprints/descriptors. The current state-of-the-art in QSAR study involves the balance of correlation approach using four different sets: training, invisible training, calibration, and validation. The final models were also validated through mean absolute error, index of ideality of correlation, Y-randomization and applicability domain analysis. R2 and Q2 values for the best model were 0.8602, 0.8507 (training); 0.8435, 0.8331 (invisible training); 0.7424, 0.7020 (calibration); 0.5993, 0.5216 (validation), respectively. The process identified some molecular substructures as good and bad fingerprints depending on their effect to increase or decrease the HRV 3Cpro inhibition. Finally, new inhibitors were designed based on the fundamental concept to replace the bad fragments with the good fragments as well as including more good fragments into the structure. The study points out the importance of the fingerprint based drug design strategy through Monte Carlo optimization method in the modelling of HRV 3Cpro inhibitors.

The drug design and discovery of lipid modulators is very demanding as no new molecule has entered into the market in the last 35 years. Cholesteryl ester transfer protein (CETP) is a promising target as lipid modulators. Inhibition of the CETP enzyme reduces the risk of cardiovascular events. The first CETP inhibitor torcetrapib and related drug candidates failed in the clinical trial due to the off-target effects leading to high toxicity. Thus, newer CETP inhibitors have now paramount importance to accelerate the drug discovery efforts in the field of cardiovascular disease (CVD). In the present study, 140 benzoxazole compounds were studied by using different chemometric techniques, for example, pharmacophore mapping, molecular docking, three-dimensional quantitative structure–activity relationship comparative molecular field analysis (3D-QSAR CoMFA), topomer CoMFA and Bayesian classification, in order to generate complete and reliable information regarding the structural requirements for the CETP inhibition. The best pharmacophore hypothesis was statistically significant (regression coefficient of 0.957 and a lower root mean square of 0.890). Molecular docking study revealed that cyano-substituted compounds form hydrogen bond with targeted macromolecule. The 3D-QSAR CoMFA model also produced a leave-one-out (LOO) cross-validated Q2 of 0.527, an R2 of 0.853 and an R2Pred of 0.603. Similarly, two topomer CoMFA models were also statistically significant and reliable in terms of their Q2, R2 and R2Pred values. The Bayesian classification study also provided the excellent ROC values of 0.919 and 0.939 for training and test sets, respectively. Overall, this study may help in the rational design of newer benzoxazole type compounds with higher CETP inhibition. Communicated by Ramaswamy H. Sarma

Breast cancer is one of the leading causes of cancers among the variety of cancers in woman all over the world. Compounds with phenylindole scaffold were found to execute promising cytotoxicity against breast cancer cell line MCF7. In the present study, a Monte Carlo based QSAR analysis was performed on a dataset containing 102 phenylindoles in order to accelerate the efforts to find out better cytotoxic phenylindoles against MCF7 cell line. The statistical qualities of the generated models were found to be quite good as far as the internal and external validation were concerned. The best models from each split (Split 1: R2 = 0.6944, Q2 = 0.6495; Split 2: R2 = 0.8202, Q2 = 0.7998; Split 3: R2 = 0.8603, Q2 = 0.8357) for the test set were selected and Y-scrambling test and applicability domain analysis were also performed to ensure the robustness of these models. Among these models, model from split 3 obtained by using hybrid descriptors (combination of SMILES and HSG with 0ECk connectivity) was used to identify and classify the structural attributes as promoters as well as hinderers of cytotoxicity for these 2-phenylindole derivatives. Results from the analysis were further used to design and predict some probable new 2-phenylindole derivatives having promising cytotoxicity (IC50

ATP-binding cassette (ABC) transporters are pivotal for cell detoxification and survival. Overexpression of ABC transporter in tumor cells lead to chemoresistance through the efflux of chemotherapeutic agents. P-glycoprotein (Pgp/ABCB1), multidrug resistance protein 1 (MRP1/ABCC1) and breast cancer resistance protein (BCRP/ABCG2) are the major ABC transporters involved in multidrug resistance (MDR) of cancer cells against anticancer drugs. ABCG2 is one of the major transporters involved in the efflux of different cytotoxic agents. Hence, inhibition of ABCG2-mediated transport is considered a prime target to resist MDR of cancer cells. Here, brief structural biology and functions of ABCG2 were discussed with the aim to identify key pharmacophoric elements to design potent and selective as well as non-toxic ABCG2 inhibitors. Structure-inhibition relationships (SIRs) of the earlier reported compounds were also explored. Taken together, this study offers insight for further development of ABCG2 inhibitors.

Graphical abstract, Highlights • Prorteases (Mpro and PLpro) are part of the replication machinery of corona virus. • Mpro and PLpro inhibitors may serve as therapeutic weapons against SARS-CoV-2. • An exquisite picture of the recent coronavirus protease inhibitors is provided. • Experimental screening approaches are also highlighted. • Challenges in the development of effective as well as drug like protease inhibitors is also discussed., Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) brutally perils physical and mental health worldwide. Unavailability of effective anti-viral drug rendering global threat of COVID-19 caused by SARS-CoV-2. In this scenario, viral protease enzymes are crucial targets for drug discovery. This extensive study meticulously focused on two viral proteases such as main protease (Mpro) and papain-like protease (PLpro), those are essential for viral replication. This review provides a detail overview of the targets (Mpro and PLpro) from a structural and medicinal chemistry point of view, together with recently reported protease inhibitors. An insight into the challenges in the development of effective as well as drug like protease inhibitors is discussed. Peptidomimetic and/or covalent coronavirus protease inhibitors possessed potent and selective active site inhibition but compromised in pharmacokinetic parameters to be a drug/drug like molecule. Lead optimization of non-peptidomimetic and/or low molecular weight compounds may be a better option for oral delivery. A masterly combination of adequate pharmacokinetic properties with coronavirus protease activity as well as selectivity will provide potential drug candidates in future. This study is a part of our endeavors which surely dictates medicinal chemistry efforts to discover effective anti-viral agent for this devastating disease.

Over the past decades, click chemistry has attracted significant attention in the preparation of cross-linked biopolymer-based hydrogels for biomedical applications. The click hydrogels offer various benefits, such as simple preparation methods, better physical stability, improved mechanical properties, ability to deliver biologics, minimal invasive administration at the target sites and sustained drug-release properties. Moreover, the use of biopolymers enhances the biodegradability, biocompatibility and nonimmunogenic properties of these hydrogels. The chapter provides a comprehensive overview of the recent advancements in click-chemistry strategies for fabricating biopolymer-based hydrogels and their drug-delivery applications.

The biocompatible, biodegradable, linear copolymer sodium alginate is fabricated from $$1 \to 4$$ linked $$\beta$$ -d-mannuronic acid (M block) and $$\alpha$$ -l-guluronic acid (G-block). It has wide applications in drug delivery, cell encapsulation, and commercial application in the textile, cosmetics, paper, food, biomedical, and pharmaceutical industries. The structure and dynamics of sodium alginate were here investigated by measuring chemical shift anisotropy (CSA) parameters, spin–lattice relaxation time, and molecular correlation time. The principal components of the CSA tensor were determined by two-dimensional phase-adjusted spinning sideband (2DPASS) cross-polarization magic angle spinning (CP-MAS) SSNMR. The alternating M and G blocks of both equatorial and axial links are associated with greater overall flexibility. The molecular correlation time of the carboxyl carbon of both G and M blocks is faster than for the anomeric carbon and pyranose carbon. This is further experimental evidence of the coexistence of two different dynamics within the polysaccharide chains of sodium alginate, which was previously established by 1H–13C dipolar profile analysis. The relaxation time of the para-crystalline region of sodium alginate is comparable with that of chitosan, but it is much shorter than that of cellulose and chitin. The order of the molecular correlation time of sodium alginate and chitosan is also the same. Hence, it can be concluded that sodium alginate exhibits greater flexibility than cellulose and chitin. These types of investigation into the local electronic configuration and nuclear spin dynamics at various carbon nuclei sites of the biopolymer at atomic-scale resolution will help in the design of biomimetic materials.

The fascinating similarity between the SARS-CoV and SARS-CoV-2, inspires scientific community to investigate deeper into the SARS-CoV proteases such as main protease (Mpro) and papain-like protease (PLpro) and their inhibitors for the discovery of SARS-CoV-2 protease inhibitors. Because of the similarity in the proteases of these two corona viruses, there is a greater chance for the previous SARS-CoV Mpro and PLpro inhibitors to provide effective results against SARS-CoV-2. In this context, the molecular fragments from the SARS-CoV protease inhibitors through the fragment-based drug design and discovery technique can be useful guidance for COVID-19 drug discovery. Here, we have focused on the structure-activity relationship studies of previous SARS-CoV protease inhibitors and discussed about crucial fragments generated from previous SARS-CoV protease inhibitors important for the lead optimization of SARS-CoV-2 protease inhibitors. This study surely offers different strategic options of lead optimization to the medicinal chemists to discover effective anti-viral agent against the devastating disease, COVID-19., Graphical abstract Image

Conventional anti-diabetes agents exhibit some undesirable side effects. Recently, lactic acidosis and/or bladder cancer were also reported with the use of these agents. Hence, there is an urgent need for alternative anti-diabetes in order to reduce/avoid the unwanted effects. In this scenario sodium glucose cotransporter 2 (SGLT2) inhibitors has already been established as an important class of anti-diabetic drug. The search for new generation SGLT2 inhibitors with high affinity is still an ongoing process. Here, we aim to develop computational models to predict the SGLT2 inhibitory activity of small molecules based on chemical structures. This work provides in-silico analysis to propose possible fragment/fingerprint identification recommended for SGLT2 inhibitors. Up-to-our knowledge, this study is an initiative to propose fingerprints responsible for SGLT2 inhibition. Furthermore, we used nine different algorithms to build machine learning (ML) models that could be used to prioritize compounds as SGLT2 inhibitors from large libraries. The best performing ML models were applied to virtually screen a large collection of FDA approved drugs. The best predicted compounds have been recommended to be biologically investigated in future in order to identify next generation SGLT2 inhibitors with different chemical structure.

Fragment based drug discovery (FBDD) by the aid of different modelling techniques have been emerged as a key drug discovery tool in the area of pharmaceutical science and technology. The merits of employing these methods, in place of other conventional molecular modelling techniques, endorsed clear detection of the possible structural fragments present in diverse set of investigated compounds and can create alternate possibilities of lead optimization in drug discovery. In this work, two fragment identification tools namely SARpy and Laplacian-corrected Bayesian analysis were used for previous SARS-CoV PLpro and 3CLpro inhibitors. A robust and predictive SARpy based fragments identification was performed which have been validated further by Laplacian-corrected Bayesian model. These comprehensive approaches have advantages since fragments are straight forward to interpret. Moreover, distinguishing the key molecular features (with respect to ECFP_6 fingerprint) revealed good or bad influences for the SARS-CoV protease inhibitory activities. Furthermore, the identified fragments could be implemented in the medicinal chemistry endeavors of COVID-19 drug discovery.

World Health Organization characterized novel coronavirus disease (COVID-19), caused by severe acute respiratory syndrome (SARS) coronavirus-2 (SARS-CoV-2) as world pandemic. This infection has been spreading alarmingly by causing huge social and economic disruption. In order to response quickly, the inhibitors already designed against different targets of previous human coronavirus infections will be a great starting point for anti-SARS-CoV-2 inhibitors. In this study, our approach integrates different ligand based drug design strategies of some in-house chemicals. The study design was composed of some major aspects: (a) classification QSAR based data mining of diverse SARS-CoV papain-like protease (PLpro) inhibitors, (b) QSAR based virtual screening (VS) to identify in-house molecules that could be effective against putative target SARS-CoV PLpro and (c) finally validation of hits through receptor-ligand interaction analysis. This approach could be used to aid in the process of COVID-19 drug discovery. It will introduce key concepts, set the stage for QSAR based screening of active molecules against putative SARS-CoV-2 PLpro enzyme. Moreover, the QSAR models reported here would be of further use to screen large database. This study will assume that the reader is approaching the field of QSAR and molecular docking based drug discovery against SARS-CoV-2 PLpro with little prior knowledge. Communicated by Ramaswamy H. Sarma

The chemical shift anisotropy tensor and site-specific spin-lattice relaxation time of folic acid were determined by a 13C 2DPASS CP-MAS NMR experiment and Torchia CP experiment respectively. The molecular correlation time at various carbon nuclei sites of folic acid was evaluated by assuming that the 13C spin-lattice relaxation mechanism is mainly governed by chemical shift anisotropy interaction and hetero-nuclear dipole–dipole coupling. CSA parameters are larger for the carbon nuclei residing at the heteroaromatic ring and aromatic ring, and those attached to double-bonded electronegative oxygen atoms. It is comparatively low for C9, C19, C21, and C22. The molecular correlation time is of the order of 10−4/10−5 s for C9, C19, C21 and C22 carbon nuclei, whereas it is of the order of 10−3 s for the rest of the carbon nuclei sites. Spin lattice relaxation time varies from 416 s to 816 s. For C23 and C14, the value is 816 s, and it is 416 s for C7 nuclei. The correlation between structure and dynamics on an atomic scale of such an important drug as folic acid can be visualized by these types of extensive spectroscopic measurements, which will help to develop an advanced drug for DNA replication.

Monte Carlo based method by using either SMILES based or combination of SMILES and Graph-based descriptors is an important strategy to build the QSAR/QSTR model for prediction of different biological endpoints. In this study, Monte Carlo based QSTR approach was applied to the dataset of 90 nitroaromatic compounds related to their in vivo toxicity, represented by 50% lethal dose concentration for rats (LD50). Both classification and regression-based QSTR models were developed to get an idea about different fingerprints for promoters and hinderers of nitroaromatics toxicity. The best classification model was obtained by using SMILES and graph-based (GAO) descriptor with 1ECK connectivity (sensitivity = 0.7143, specificity = 1.0000, accuracy = 0.8889, and MCC = 0.7774). The best regression model calculated by using SMILES and hydrogen-suppressed graph descriptors with 0ECk connectivity (R2 = 0.7386, Q2 = 0.6315, S = 0.467, and MAE = 0.340). Finally, a consensus QSTR model was generated to predict efficiently the toxicity of new compounds. The study highlighted that the comparative QSTR models by using the Monte Carlo method can also be generated and will be a useful tool for structural fingerprint analysis in case of nitroaromatics for preliminary evaluation of its toxicity to mammals.

Monte Carlo based method by using either SMILES based or combination of SMILES and Graph-based descriptors is an important strategy to build the QSAR/QSTR model for prediction of different biological endpoints. In this study, Monte Carlo based QSTR approach was applied to the dataset of 90 nitroaromatic compounds related to their

Human rhinoviruses (HRVs), a major cause of common cold and upper respiratory infections, may trigger severe respiratory complications like asthma and COPD. To date, no drugs are available in the market which are designed as novel HRV inhibitors despite the involvement of some pharmaceutical companies' due to economical and clinical constraints. HRV 3C protease may be a potential target for drug design as it plays crucial role in viral RNA replication and virion assembly process. Therefore, designing novel HRV 3Cpro inhibitors is necessary and demanding in the field of antiviral drug design. In this article, statistically significant and validated classification-based QSARs of a series of HRV 3Cpro inhibitors were performed for the first time as per the authors' knowledge. Results suggest that oxopyrrolidine and piperidinone rings are favored whereas carboxybenzyl and unsubstituted benzyl functions may be unfavorable. Moreover, this group, along with cyclic alkyl or aryl ring structures may favor HRV 3Cpro inhibition. These observations may be utilized for the design of a higher active anti-HRV agent in future.

Human carbonic anhydrase-II (hCA-II) is the most dominant physiologic isoform amongst the sixteen reported hCA isoforms. Because of its high availability in the different anatomical, and cellular sites of the eye like retina and lens, it plays a more prominent role in the regulation of intraocular pressure than the other twelve catalytically active hCA isoforms. This isoform is also located in the brain, kidney, gastric mucosa, osteoclasts, RBCs, skeletal muscle, testes, pancreas, lungs, etc. Earlier, hCA-II inhibitors were designed based on the sulfonamides e.g. acetazolamide, dichlorphenamide, methazolamide, ethoxzolamide, etc. and they were used systemically in antiglaucoma therapy. Many successful attempts have been made by the researchers in order to design more potent and effective inhibitors by incorporating various moieties in sulphonamides. Some novel scaffolds like chalcones, thiophenes, organotellurium compounds, dithiocarbamate, selenide, and 2-benzylpyrazine, etc. were also designed as hCA-II inhibitors and their inhibitory efficacy was proved in the nanomolar range. In order to obtain relevant information from the insights of their structure-activity relationship, the reported hCA-II inhibitors from the year 1989 to 2019 were critically analysed. It gave a complete insight into the relationship between their structure-activity and hCA-II inhibition. The broad spectrum of our investigation may help researchers to summarize all the crucial structural information required for the development of more potent hCA-II inhibitors for glaucoma.