Current Computer - Aided Drug Design - Volume 18, Issue 1, 2022

Background: Many techniques to design chemical libraries for screening have been put forward over time. General use libraries are still important when screening against novel targets, and their design has relied on the use of molecular descriptors. In contrast, chemotype or scaffold analysis has been used less often. Objective: We describe a simple method to assess chemical diversity based on counts of the chemotypes that offers an alternative to model chemical diversity. We describe a simple method to assess chemical diversity based on counts of the chemotypes that offers an alternative to model chemical diversity based on computed molecular properties. We show how chemotype counts can be used to evaluate the diversity of a library and compare diversity selection algorithms. We demonstrate an efficient compound selection algorithm based on chemotype analysis. Methods: We use automated chemotype perception algorithms and compare them to traditional techniques for diversity analysis to check their effectiveness in designing diverse libraries for screening. Results: The best type of molecular fingerprints for diversity selection in our analysis are extended circular fingerprints, but they can be outperformed by the use of a chemotype diversity algorithm, which can be more intuitive than traditional techniques based on molecular descriptors. Chemotype- -based algorithms retrieve a larger share of the chemotypes contained in a library when picking a subset of the chemicals in a collection. Conclusions: Chemotype analysis offers an alternative for the generation of a general-purpose screening library as it maximizes the number of chemotypes present in a subset with the smallest number of compounds. The applications of methods based on chemotype analysis that does not resort to the use of molecular descriptors are a very promising but seldom explored area of chemoinformatics.

Background: Diabetes mellitus is a chronic metabolic disorder, characterized by hyperglycemia over a prolonged period, disturbance of fat, protein, and carbohydrate metabolism, resulting from defective insulin secretion, insulin action or both. Dipeptidyl peptidase-IV (DPP-IV) inhibitors are relatively a new class of oral hypoglycemic agents that reduce the deterioration of gutderived endogenous incretin hormones secreted in response to food ingestion to stimulate the secretion of insulin from beta cells of the pancreas. Objective: In this study, synthesis, characterization, and biological assessment of twelve novel phenanthridine sulfonamide derivatives 3a-3l as potential DPP-IV inhibitors were carried out. The target compounds were docked to study the molecular interactions and binding affinities against the DPP-IV enzyme. Methods: The synthesized molecules were characterized using 1H-NMR, 13C-NMR, IR, and MS. Quantum-polarized ligand docking (QPLD) was also performed. Results: In vitro biological evaluation of compounds 3a-3l reveals comparable DPP-IV inhibitory activities ranging from 10%-46% at 100 μM concentration, where compound 3d harboring ortho- fluoro moiety exhibited the highest inhibitory activity. QPLD study shows that compounds 3a-3l accommodate DPP-IV binding site and form H-bonding with the R125, E205, E206, S209, F357, R358, K554, W629, S630, Y631, Y662, R669, and Y752 backbones Conclusion: In conclusion, phenanthridine sulfonamides could serve as potential DPP-IV inhibitors that require further structural optimization in order to enhance their inhibitory activity.

Introduction: The outburst of the novel coronavirus COVID-19, at the end of December 2019 has turned into a pandemic, risking many human lives. The causal agent being SARS-CoV-2, a member of the long-known Coronaviridae family, is a positive-sense single-stranded enveloped virus and closely related to SARS-CoV. It has become the need of the hour to understand the pathophysiology of this disease, so that drugs, vaccines, treatment regimens and plausible therapeutic agents can be produced. Methods: In this regard, recent studies uncovered the fact that the viral genome of SARS-CoV-2 encodes non-structural proteins like RNA-dependent RNA polymerase (RdRp) which is an important tool for its transcription and replication process. A large number of nucleic acid-based anti-viral drugs are being repurposed for treating COVID-19 targeting RdRp. Few of them are at the advanced stage of clinical trials, including remdesivir. While performing a detailed investigation of the large set of nucleic acid-based drugs, we were surprised to find that the synthetic nucleic acid backbone has been explored very little or rare. Results: We designed scaffolds derived from peptide nucleic acid (PNA) and subjected them to in- -silico screening systematically. These designed molecules have demonstrated excellent binding towards RdRp. Compound 12 was found to possess a similar binding affinity as remdesivir with comparable pharmacokinetics. However, the in-silico toxicity prediction indicates that compound 12 may be a superior molecule which can be explored further due to its excellent safety-profile with LD50 12,000mg/kg as opposed to remdesivir (LD50 =1000mg/kg). Conclusion: Compound 12 falls in the safe category of class 6. Synthetic feasibility, equipotent binding and very low toxicity of this peptide nucleic acid-derived compound can make it a leading scaffold to design, synthesize and evaluate many similar compounds for the treatment of COVID-19.

Aim: This study aimed at screening and development of TG2 inhibitors as anti lung cancer agent. Background: Transglutaminase 2 (TG2) is multifunctional and ubiquitously expressed protein from the transglutaminase family. It takes part in various cellular processes and plays an important role in the pathogenesis of autoimmune, neurodegerative diseases, and also cancer. Objective: The proposed study focused on screening potent inhibitors of TG2 by in-silico method and synthesize their derivative as well as analyse its activity by utilizing an in-vitro approach. Materials and Methods: Molecular docking studies have been carried out on the different classes of TG2 inhibitors against the target protein. Nearly thirty TG2 inhibitors were selected from literature and docking was performed against transglutaminase 2. The computational ADME property screening was also carried out to check their pharmacokinetic properties. The compounds which exhibited positive ADME properties with good interaction while possessing the least binding energy were further validated for their anti-lung cancer inhibition property against A549 cell lines using cytotoxicity studies. Results: The results of the present study indicate that the docked complex formed by cystamine showed better binding affinity towards target protein, so this derivative of cystamine was formed using 2,5 dihydrobenzoic acid. Invitro results revealed that both molecules proved to be good cytotoxic agents against A549 lung cancer (875.10, 553.22 μg/ml), respectively. Further, their activity needs to be validated on TG2 expressing lung cancer. Conclusion: Cystamine and its derivative can act as a potential therapeutic target for lung cancer but its activity should be further validated on TG2 expressing lung cancer.

Background: Pyrrole compounds having a heterocyclic structure are the most researched and biological activities such as antioxidant and anticancer activities. Objective: Herein is a first effort to study the significance of heterocyclic compounds to include pyrrole and triazolidine-3,5-dion moiety, on the pharmacokinetic, antioxidant activity and cytotoxic activity on MCF-7 and MCF-12A cell lines. Method: The molecular structures of compounds I-XIV were simulated by the theoretical B3- LYP/DFT method. Pharmacokinetic studies of PhTAD-substituted heterocyclic compounds (IXIV) were analyzed to show Lipinski's rules via in-silico methods of Swiss-ADME. The drug likeness calculations were carried out in Molinspiration analyses. Some toxicity risk parameter can be quantified using Osiris. Antioxidant activities determined by DPPH, Fe+2 ions chelating and reducing. Cytotoxic activity measured by MTT and RTCA Results: Compared with the DPPH activity, the metal chelating activity exhibited serious similar antioxidant effects by PhTAD substituted pyrrole compounds. The same compounds showed the highest activity among the two antioxidant activities. The IC50 values of the compounds are in the range of 12 and 290 μM in the MCF-7 cell line. In the MTT and RTCA assays, All compounds showed cytotoxic activity, but about half of the fourteen compounds showed high cytotoxicity. IC50 values of the compounds are in the range of 5 and 54 μM for MTT and range of 1.5 and 44 μM for RTCA. Conclusion: Data of the antioxidant and cytotoxic activity of PhTAD-substituted dihydropyrrole- derived compounds in MCF-7 and MCF-12A cell lines confirmed that the compounds are biologically active compound and are notable for anti-cancer researches.

Background: The prediction of drug-protein interaction (DPI) plays an important role in drug discovery and repositioning. Unfortunately, traditional experimental validation of DPIs is expensive and time-consuming. Therefore, it is necessary to develop in silico methods for the identification of potential DPIs. Methods: In this work, the identification of DPIs was performed by the generated recommendation of the unexplored interaction of the drug-protein bipartite graph. Three kinds of recommenders were proposed to predict the potential DPIs. Results: The simulation results showed that the proposed models obtained good performance in crossvalidation and independent test. Conclusion: Our recommendation strategy based on collaborative filtering can effectively improve the DPI identification performance, especially for certain DPIs lacking chemical structure similarity or genomic sequence similarity.

Background: The discovery of therapeutic anticancer agents based on natural products is one of the current research focuses. Network pharmacology will broaden our understanding of drug actions by bioinformatics analysis. Objective: To explore the potential and provide scientific evidence for methylaervine as a lead compound against cervical carcinoma. Methods: Methylaervine was synthesized, and its activity against four cancer cell lines was evaluated by MTT assay. Pharmacokinetic properties were obtained by in silico approaches, and the pharmacologic mechanism was predicted by network pharmacology. Then we validated and investigated our predictions of candidate targets using a molecular docking study. Results: Methylaervine was synthesized with a total yield of 54.9%, which displayed activity against HeLa (IC50 = 14.8 μM) with good predicted pharmacokinetic properties, thus it was considered a potential lead compound. The network pharmacology study indicated that methylaervine could act against cervical carcinoma by regulating the function of multiple pivotal targets, such as CTNNB1, PTPRJ, RPA1, and TJP1, mainly covering cell growth, cell motility, and cell proliferation. Moreover, docking analysis showed that hydrogen bonds and hydrophobic interactions were the main forms of interactions. Conclusion: This work would provide new insight into the design of anti-cervical carcinoma drugs based on methylaervine.