FAF-Drugs: free ADME/tox filtering of compound collections

Molecular Gas-Phase Conformational Ensembles

Accurately determining the global minima of a molecular structure is important in diverse scientific fields, including drug design, materials science, and chemical synthesis. Conformational search engines serve as valuable tools for exploring the extensive conformational space of molecules and for identifying energetically favorable conformations. In this study, we present a comparison of Auto3D, CREST, Balloon, and ETKDG (from RDKit), which are freely available conformational search engines, to evaluate their effectiveness in locating global minima. These engines employ distinct methodologies, including machine learning (ML) potential-based, semiempirical, and force field-based approaches. To validate these methods, we propose the use of collisional cross-section (CCS) values obtained from ion mobility-mass spectrometry studies. We hypothesize that experimental gas-phase CCS values can provide experimental evidence that we likely have the global minimum for a given molecule. To facilitate this effort, we used our gas-phase conformation library (GPCL) which currently consists of the full ensembles of 20 small molecules and can be used by the community to validate any conformational search engine. Further members of the GPCL can be readily created for any molecule of interest using our standard workflow used to compute CCS values, expanding the ability of the GPCL in validation exercises. These innovative validation techniques enhance our understanding of the conformational landscape and provide valuable insights into the performance of conformational generation engines. Our findings shed light on the strengths and limitations of each search engine, enabling informed decisions for their utilization in various scientific fields, where accurate molecular structure determination is crucial for understanding biological activity and designing targeted interventions. By facilitating the identification of reliable conformations, this study significantly contributes to enhancing the efficiency and accuracy of molecular structure determination, with particular focus on metabolite structure elucidation. The findings of this research also provide valuable insights for developing effective workflows for predicting the structures of unknown compounds with high precision.

Exploring the pharmacological mechanisms and key active ingredients of total flavonoids from Lamiophlomis rotata (Benth.) Kudo against rheumatoid arthritis based on multi-technology integrated network pharmacology

ETHNOPHARMACOLOGICAL RELEVANCE

Lamiophlomis rotata (Benth.) Kudo (LR, Lamiaceae) is a traditional Tibetan medicinal material in China. Tibetan medicine classic and research report suggested that LR could be used to cure rheumatoid arthritis (RA). However, the anti-RA active ingredients and pharmacological mechanisms of LR have not been elucidated.

AIM OF THE STUDY

To explore the mechanisms and key active ingredients of total flavonoids from LR (TFLR) against RA.

MATERIALS AND METHODS

First, the mechanisms of TFLR against RA were investigated on collagen-induced arthritis (CIA) rat model by analyzing paw appearance, paw swelling, arthritis score, spleen index, thymus index, inflammatory cytokine (TNF-α, IL-1β, IL-6 and IL-17) levels in serum, histopathology of ankle joint and synovium from knee joint (hematoxylin-eosin, safranin O-fast green and DAB-TUNEL staining), and apoptosis-related protein (PI3K, Akt1, p-Akt, Bad, p-Bad, Bcl-xL and Bcl-2) levels in the synovium of ankle joints (Western blot). Then, the crucially active ingredients of TFLR against RA were explored by network pharmacology, ingredient analysis, in vitro metabolism and TNF-α-induced human RA synovial fibroblast MH7A proliferation assays. Network pharmacology was applied to predict the key active ingredients of TFLR against RA. The ingredient analysis and in vitro metabolism of TFLR were performed on HPLC, and MH7A proliferation assay were applied to evaluate the predicted results of network pharmacology.

RESULTS

TFLR shown excellently anti-RA effect by reducing paw swelling, arthritis score, spleen index, thymus index and inflammatory cytokine (IL-1β, IL-6 and IL-17) levels, and improving the histopathological changes of ankle joint and synovium from knee joint in CIA rats. Results of Western blot indicated that TFLR reversed the changes of PI3K, p-Akt, p-Bad, Bcl-xL and Bcl-2 levels in the ankle joint synovium of CIA rats. Results of network pharmacology exhibited that luteolin was identified as the pivotal active ingredient of TFLR against RA. The ingredient analysis of TFLR indicated that the main ingredient in TFLR was luteoloside. The in vitro metabolism study of TFLR suggested that luteoloside could be converted to luteolin in artificial gastric juice and intestinal juice. Results of MH7A proliferation assay showed that there was no significant difference between TFLR and equal luteoloside on the viability of MH7A cells, indicating that luteoloside was the key active ingredient of TFLR against RA. Additionally, the luteolin (same mol as luteoloside) showed better inhibitory effect on the viability of MH7A cells than luteoloside.

CONCLUSION

TFLR showed anti-RA effect, and the mechanism was related to promoting synovial cell apoptosis mediated by PI3K/Akt/Bad pathway. Meanwhile, this work indicated that luteoloside was the key active ingredient of TFLR against RA. This work lays a foundation for providing TFLR product with clear mechanism and stable quality to treat RA.

Improving on in-silico prediction of oral drug bioavailability

INTRODUCTION

Although significant development has been made in high-throughput screening of oral drug absorption and oral bioavailability, in silico prediction continues to play an important role in prediction of oral bioavailability and assisting in the proper selection of potential drug candidates.

AREAS COVERED

This review describes the improvements and latest modeling methods and algorithms available for the prediction of this important parameter. We performed a PubMed database search with a focus on the literature published in the last 15 years.

EXPERT OPINION

A tremendous efforts have been done in the past several years to develop reliable prediction tools that can provide accurate prediction for oral bioavailability. Several studies demonstrated new methodologies and techniques to develop either web-based in silico predictive tools or integrated PBPK models to predict oral bioavailability for new molecules. Improvements in the databases and the computational power will enhance the in silico prediction accuracy and reliability. Finally, introducing artificial intelligence to the drug development process will help improve the prediction tools.

Piperidine-based natural products targeting Type IV pili antivirulence: A computational approach

Type IV (T4) pilus is among the virulence factors with a key role in serious bacterial diseases. Specifically, in Neisseria meningitidis and Pseudomonas aeruginosa, it determines pathogenicity and causes infection. Here, a computational approach has been pursued to find piperidine-based inhibitor molecules against the elongation ATPase of T4 pili in these two selected pathogens. Using the modeled structures of the PilF and PilB ATPases of N. meningitidis and P. aeruginosa, virtual library screening via molecular docking has returned inhibitor molecule candidates. The dynamics of the best three binders have further been investigated in detail via molecular dynamic simulations. Among these, ligands with COCONUT IDs CNP0030078 and CNP0051517 were found to have higher potential in the inhibition of ATPases based on molecular dynamic simulation analysis and biological activity information. The obtained results will guide future efforts in antivirulence drug development against T4 pili of N. meningitidis and P. aeruginosa.

Computational approaches for the design of modulators targeting protein-protein interactions

BACKGROUND

Protein-protein interactions (PPIs) are intriguing targets for designing novel small-molecule inhibitors. The role of PPIs in various infectious and neurodegenerative disorders makes them potential therapeutic targets . Despite being portrayed as undruggable targets, due to their flat surfaces, disorderedness, and lack of grooves. Recent progresses in computational biology have led researchers to reconsider PPIs in drug discovery.

AREAS COVERED

In this review, we introduce in-silico methods used to identify PPI interfaces and present an in-depth overview of various computational methodologies that are successfully applied to annotate the PPIs. We also discuss several successful case studies that use computational tools to understand PPIs modulation and their key roles in various physiological processes.

EXPERT OPINION

Computational methods face challenges due to the inherent flexibility of proteins, which makes them expensive, and result in the use of rigid models. This problem becomes more significant in PPIs due to their flexible and flat interfaces. Computational methods like molecular dynamics (MD) simulation and machine learning can integrate the chemical structure data into biochemical and can be used for target identification and modulation. These computational methodologies have been crucial in understanding the structure of PPIs, designing PPI modulators, discovering new drug targets, and predicting treatment outcomes.

Evaluation of Free Online ADMET Tools for Academic or Small Biotech Environments

For a new molecular entity (NME) to become a drug, it is not only essential to have the right biological activity also be safe and efficient, but it is also required to have a favorable pharmacokinetic profile including toxicity (ADMET). Consequently, there is a need to predict, during the early stages of development, the ADMET properties to increase the success rate of compounds reaching the lead optimization process. Since Lipinski's rule of five, the prediction of pharmacokinetic parameters has evolved towards the current in silico tools based on empirical approaches or molecular modeling. The commercial specialized software for performing such predictions, which is usually costly, is, in many cases, not among the possibilities for research laboratories in academia or at small biotech companies. Nevertheless, in recent years, many free online tools have become available, allowing, more or less accurately, for the prediction of the most relevant pharmacokinetic parameters. This paper studies 18 free web servers capable of predicting ADMET properties and analyzed their advantages and disadvantages, their model-based calculations, and their degree of accuracy by considering the experimental data reported for a set of 24 FDA-approved tyrosine kinase inhibitors (TKIs) as a model of a research project.

Exploring potential inhibitors against Kyasanur forest disease by utilizing molecular dynamics simulations and ensemble docking

Kyasanur forest disease (KFD) is a tick-borne, neglected tropical disease, caused by KFD virus (KFDV) which belongs to Flavivirus (Flaviviridae family). This emerging viral disease is a major threat to humans. Currently, vaccination is the only controlling method against the KFDV, and its effectiveness is very low. An effective control strategy is required to combat this emerging tropical disease using the existing resources. In this regard, in silico drug repurposing method offers an effective strategy to find suitable antiviral drugs against KFDV proteins. Drug repurposing is an effective strategy to identify new use for approved or investigational drugs that are outside the scope of their initial usage and the repurposed drugs have lower risk and higher safety compared to de novo developed drugs, because their toxicity and safety issues are profoundly investigated during the preclinical trials in human/other models. In the present work, we evaluated the effectiveness of the FDA approved and natural compounds against KFDV proteins using in silico molecular docking and molecular simulations. At present, no experimentally solved 3D structures for the KFD viral proteins are available in Protein Data Bank and hence their homology model was developed and used for the analysis. The present analysis successfully developed the reliable homology model of NS3 of KFDV, in terms of geometry and energy contour. Further, in silico molecular docking and molecular dynamics simulations successfully presented four FDA approved drugs and one natural compound against the NS3 homology model of KFDV. Communicated by Ramaswamy H. Sarma.

Molecular docking and simulation studies of flavonoid compounds against PBP-2a of methicillin-resistant Staphylococcus aureus

Methicillin-Resistant Staphylococcus aureus (MRSA), a pathogenic bacterium that causes life-threatening outbreaks such as community-onset and nosocomial infections as emerging 'superbug'. Time and motion study of its virulent property developed resistance against most of the antibiotics such as Vancomycin. Thereby, to curb this problem entails the development of new therapeutic agents. Plant-derived antimicrobial agents have recently piqued people's interest, so in this research, 186 flavonoids compound selected to unmask the best candidates that can act as potent inhibitors against the Penicillin Binding Protein-2a (PBP-2a) of MRSA. Molecular docking performed using PyRx and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the PBP-2a. The selected candidates strongly interact with the different amino acid residues. The 30 ns molecular dynamics (MD) simulations with five top-ranked compounds such as Naringin, Hesperidin, Neohesperidin, Didymin and Icariin validated the docking interactions. These findings are also strongly supported by root-mean-square deviation, root-mean-square fluctuation and the radius of gyration. ADME/T analysis demonstrates that these candidates appear to be safer inhibitors. Our findings point to natural flavonoids as a promising and readily available source of adjuvant antimicrobial therapy against resistant strains in the future.Communicated by Ramaswamy H. Sarma.

Integrated strategy of network pharmacology, molecular docking, HPLC-DAD and mice model for exploring active ingredients and pharmacological mechanisms of Penthorum chinense Pursh against alcoholic liver injury

ETHNOPHARMACOLOGICAL RELEVANCE

Penthorum chinense Pursh (PCP, Saxifragaceae) is an edible plant and frequently-used Chinese herbal medicine, and is commonly used as Miao medicine in China. It showed well effect on alcoholic liver injury (ALI), but studies on its active ingredients and mechanisms against ALI remain at the starting stage.

AIM OF THE STUDY

This work aims to explore the active ingredients and pharmacological mechanisms of PCP against ALI.

MATERIALS AND METHODS

First, network pharmacology was applied to decipher the potential active ingredients and pharmacological mechanisms of PCP against ALI by ingredient identification, ADMET evaluation, target identification, network construction and analysis, protein-protein interaction (PPI) analysis, and gene enrichment analysis. Second, molecular docking was used to explore the interaction between key active ingredient and hub protein of PCP against ALI. Then, the ingredient analysis of PCP aqueous extract and semiquantitative analysis of key active ingredient were carried out on HPLC-DAD. Subsequently, mice with ALI were used to investigate the therapeutic effect or verify the predicted mechanisms of PCP or key active ingredient against ALI by analyzing body weight, liver index, ALT and AST activities in serum and liver tissues, oxidation related indices (SOD activity, GSH level and MDA level) in liver tissues, histopathology of liver tissues (oil red O, hematoxylin-eosin and DAB-TUNEL staining), and changes of related proteins (PI3K, Akt, p-Akt, Bax and Bcl-2) in liver tissues with the aid of Western blot.

RESULTS

Network pharmacology showed that the active ingredients and related genes of PCP against ALI comprised 10 ingredients and 52 genes. Based on the result of ingredient analysis of PCP aqueous extract, quercitrin was identified as the key active ingredient of PCP against ALI. PPI analysis indicated that AKT1 was the hub gene of PCP against ALI, and molecular docking suggested that there were good interaction between quercetin and Akt1 protein. Gene enrichment analysis showed that the pivotal molecular mechanism of PCP against ALI might be to inhibit hepatocyte apoptosis via activation of PI3K-Akt signaling pathway. PCP and quercitrin showed anti-ALI effect by offsetting weight loss and increase of liver index, and reversing the imbalance of oxidative stress and histopathological changes of liver tissues (abnormal fatty acid metabolism, hepatic cord swelling and inflammatory cell infiltration) in mice with ALI. PCP caused the decrease of DAB-TUNEL-positive cells, upregulated the anti-apoptotic proteins (PI3K, Akt and p-Akt) levels and the ratio of p-Akt/Akt, and downregulated pro-apoptotic protein (Bax) level and the ratio of Bax/Bcl-2 in liver tissues of mice with ALI, indicating that the mechanism of PCP against ALI involved in inhibiting hepatocyte apoptosis via activation of PI3K-Akt signaling pathway.

CONCLUSION

PCP and quercitrin showed well anti-ALI effect. The key active ingredient of PCP against ALI was identified as quercitrin. The underlying pharmacological mechanisms of PCP against ALI may be related to PI3K-Akt signaling pathway-mediated inhibition of hepatocyte apoptosis. This work provided new evidence to support the application of PCP in treatment of ALI, and a research basis for the research and development of functional foods or drugs against ALI from PCP.

Application of machine learning to predict the inhibitory activity of organic chemicals on thyroid stimulating hormone receptor

With the promotion of carbon neutrality, it is also important to synchronously promote the assessment and sustainable management of chemicals so as to protect public health. Humans and animals are possibly exposed to endocrine disruptors that have inhibitory effects on thyroid stimulating hormone receptor (TSHR). As such, it is important to identify chemicals that inhibit TSHR and to develop models to predict their inhibitory activity. In this study, 5952 compounds derived from a cyclic adenosine monophosphate (cAMP) analysis, a key signaling pathway in thyrocytes, were used to establish a binary classification model comparing methods that included random forest (RF), extreme gradient boosting (XGB), and logistic regression (LR). The prediction model based on RF showed the highest identification accuracy for revealing chemicals that may inhibit TSHR. For the RF model, recall was calculated at 0.89, balance accuracy was 0.85, and its receiver operating characteristic (ROC) curve-area under (AUC) was 0.92, indicating that the model had very high predictive capacity. The lowest CDocker energy (CE) and CDocker interaction energy (CIE) for chemicals and TSHR were determined and were subsequently introduced into the predictive model as descriptors. A regression model, extreme gradient boosting-Regression (XGBR), was successfully established yielding an R² = 0.65 to predict inhibitory activity for active compounds. Parameters that included dissociation characteristics, molecular structure, and binding energy were all key factors in the predictive model. We demonstrate that QSAR models are useful approaches, not only for identifying chemicals that inhibit TSHR, but for predicting inhibitory activity of active compounds.

In Vitro and In Silico Antistaphylococcal Activity of Indole Alkaloids Isolated from Tabernaemontana cymosa Jacq (Apocynaceae)

The species of the genus Tabernaemontana have a long tradition of use in different pathologies of infectious origins; the antibacterial, antifungal, and antiviral effects related to the control of the pathologies where the species of this genus are used, have been attributed to the indole monoterpene alkaloids, mainly those of the iboga type. There are more than 1000 alkaloids isolated from different species of Tabernaemontana and other genera of the Apocynaceae family, several of which lack studies related to antibacterial activity. In the present study, four monoterpene indole alkaloids were isolated from the seeds of the species Tabernaemontana cymosa Jacq, namely voacangine (1), voacangine-7-hydroxyindolenine (2), 3-oxovoacangine (3), and rupicoline (4), which were tested in an in vitro antibacterial activity study against the bacteria S. aureus, sensitive and resistant to methicillin, and classified by the World Health Organization as critical for the investigation of new antibiotics. Of the four alkaloids tested, only voacangine was active against S. aureus, with an MIC of 50 µg/mL. In addition, an in silico study was carried out between the four isolated alkaloids and some proteins of this bacterium, finding that voacangine also showed binding to proteins involved in cell wall synthesis, mainly PBP2 and PBP2a.

Computer-aided identification of potential inhibitors against Necator americanus glutathione S-transferase 3

Hookworm infection is caused by the blood-feeding hookworm gastrointestinal nematodes. Its harmful effects include anemia and retarded growth and are common in the tropics. A current control method involves the mass drug administration of synthetic drugs, mainly albendazole and mebendazole. There are however concerns of low efficacy and drug resistance due to their repeated and excessive use. Although, Necator americanus glutathione S-transferase 3 (Na-GST-3) is a notable target, using natural product libraries for computational elucidation of promising leads is underexploited. This study sought to use pharmacoinformatics techniques to identify compounds of natural origins with the potential to be further optimized as promising inhibitors. A compendium of 3182 African natural products together with five known helminth GST inhibitors including Cibacron blue was screened against the active sites of the Na-GST-3 structure (PDB ID: 3W8S). The hit compounds were profiled to ascertain the mechanisms of binding, anthelmintic bioactivity, physicochemical and pharmacokinetic properties. The AutoDock Vina docking protocol was validated by obtaining 0.731 as the area under the curve calculated via the receiver operating characteristics curve. Four compounds comprising ZINC85999636, ZINC35418176, ZINC14825190, and Dammarane Triterpene13 were identified as potential lead compounds with binding energies less than -9.0 kcal/mol. Furthermore, the selected compounds formed key intermolecular interactions with critical residues Tyr95, Gly13 and Ala14. Notably, ZINC85999636, ZINC14825190, and dammarane triterpene13 were predicted as anthelmintics, whilst all the four molecules shared structural similarities with known inhibitors. Molecular modelling showed that the compounds had reasonably good binding free energies. More so, they had high binding affinities when screened against other variants of the Na-GST, namely Na-GST-1 and Na-GST-2. Ligand quality assessment using ligand efficiency dependent lipophilicity, ligand efficiency, ligand efficiency scale and fit quality scale showed the molecules are worthy candidates for further optimization. The inhibitory potentials of the molecules warrant in vitro studies to evaluate their effect on the heme regulation mechanisms.

MEMES: Machine learning framework for Enhanced MolEcular Screening

In drug discovery applications, high throughput virtual screening exercises are routinely performed to determine an initial set of candidate molecules referred to as "hits". In such an experiment, each molecule from a large small-molecule drug library is evaluated in terms of physical properties such as the docking score against a target receptor. In real-life drug discovery experiments, drug libraries are extremely large but still there is only a minor representation of the essentially infinite chemical space, and evaluation of physical properties for each molecule in the library is not computationally feasible. In the current study, a novel Machine learning framework for Enhanced MolEcular Screening (MEMES) based on Bayesian optimization is proposed for efficient sampling of the chemical space. The proposed framework is demonstrated to identify 90% of the top-1000 molecules from a molecular library of size about 100 million, while calculating the docking score only for about 6% of the complete library. We believe that such a framework would tremendously help to reduce the computational effort in not only drug-discovery but also areas that require such high-throughput experiments.

Exploration of interaction behavior between spiro[indene-2,2'-[1,3,5]oxathiazine]-1,3-diones and DNA with the help of DFT, molecular docking, and MD simulations

A detailed computational study covering density functional theory (DFT), molecular docking, and molecular dynamics (MD) simulations of some spirocyclic compounds interacting with a B-DNA has been performed. DFT calculations were performed using the B3LYP functional with 6-311++G(d,p) basis set and were used to identify the electrophilic and nucleophilic centers in electrostatic forces. NMR results were in agreement with previous experimental data and approved the reliability of the used method and basis set. The in silico screening results showed that spirocyclic compounds fulfill the Lipinski's rule of five and can be developed as potential oral bioavailable drug candidates. Based on molecular docking results, the binding affinities follow the 4c < 4d < 4a = 4b < 4e < 4g < 4f order and ranged from -8.6 to -9.7 kcal/mol indicating a reasonably favorable interaction between DNA and investigated compounds. The adducts were stabilized by hydrophobic and hydrogen bonding interactions. The MD simulations performed for 100 ns and the results are reported in terms of variables such as root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), center of mass (COM) separation distance between DNA and ligands, intermolecular hydrogen bonds, and radial distribution functions (RDF). The MD simulations demonstrated that compounds 4a and 4d bind into the minor groove of 1BNA and may act as potential biological probes for B-DNA.Communicated by Ramaswamy H. Sarma.

Antidepressant activity of rose oxide essential oil: possible involvement of serotonergic transmission

Rose oxide (RO) is a monoterpene found in rose oil fragrances. This monoterpene has been reported to possess anti-inflammatory activity, however, little is known regarding its pharmacological activity. The present study was carried out to evaluate its antidepressant action and possible mechanisms of action. Analysis of ADMET pharmacokinetic properties (absorption, distribution, metabolism, excretion and toxicity) of rose oxide was performed by computational prediction analysis. Behavioral tests were performed to assess the interaction between rose oxide and the central nervous system and antidepressant effect that includes: forced swim test (FST), tail suspension test (TST), open field test (OFT) and rota-rod test. The results of pharmacokinetic and toxicological properties indicate that rose oxide could be used orally, since it has good intestinal absorption as well as pharmacological and toxicological properties that can be similar to pharmacological compounds (regular hepatic metabolism and low toxicity). Treatment with 50 mg/kg of rose oxide was able to decrease the immobility time of animals not affected by FST and TST and was not able to alter the motor activity of the OFT and rota-rod test, suggesting modulation and antidepressant activity. Docking data suggest that rose oxide can bind to receptors in the serotonergic pathway. The results described here suggest that rose oxide has antidepressant activity, modulating the serotonergic pathway.

Applications of Virtual Screening in Bioprospecting: Facts, Shifts, and Perspectives to Explore the Chemo-Structural Diversity of Natural Products

Natural products are continually explored in the development of new bioactive compounds with industrial applications, attracting the attention of scientific research efforts due to their pharmacophore-like structures, pharmacokinetic properties, and unique chemical space. The systematic search for natural sources to obtain valuable molecules to develop products with commercial value and industrial purposes remains the most challenging task in bioprospecting. Virtual screening strategies have innovated the discovery of novel bioactive molecules assessing in silico large compound libraries, favoring the analysis of their chemical space, pharmacodynamics, and their pharmacokinetic properties, thus leading to the reduction of financial efforts, infrastructure, and time involved in the process of discovering new chemical entities. Herein, we discuss the computational approaches and methods developed to explore the chemo-structural diversity of natural products, focusing on the main paradigms involved in the discovery and screening of bioactive compounds from natural sources, placing particular emphasis on artificial intelligence, cheminformatics methods, and big data analyses.

Cameroonian medicinal plants as potential candidates of SARS-CoV-2 inhibitors

Coronavirus disease 2019 (COVID-19) is an ongoing pandemic instigated by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) which changed the daily train of the world's population and cause several dead. Despite the significant efforts made in developing vaccines and therapeutic drugs, there is currently no available effective treatment against this new coronavirus infection, hence the need to continue research which is aimed at limiting the progression of this virus. The present study which has as objective to carry out in silico studies on the metabolites of some Cameroonian medicinal plants of the Asteraceae family with a view to propose potential molecules to fight against COVID-19. The selected plants are commonly used to treat respiratory infectious diseases, and for this reason they may contain some constituents which could exhibit an antiviral activity against SARS-CoV-2. In this work, a set of 74 naturally occurring compounds are computed with SARS-CoV-2 main protease protein (PDB ID: 6lu7) and spike protein (PDB ID: 6m0j) for their affinity and stability using binding energy analysis and molecular docking. Chrysoeriol-7-O-β-D-glucuronopyranoside (compound 16) has showed promising results including excellent Absorption, Distribution, Metabolism and Excretion (ADME) parameters as well as insignificant toxicity. Finally, the stability of this compound is complex with the two proteins validated through molecular dynamics (MD) simulation, they displayed stable trajectory and molecular properties with consistent interaction profile in molecular dynamics simulations. These findings call for further in vitro and in vivo challenges of phytoconstituents against the COVID-19 as a potential agent to fight the spread of this dramatic pandemic.Communicated by Ramaswamy H. Sarma.

Screening possible drug molecules for Covid-19. The example of vanadium (III/IV/V) complex molecules with computational chemistry and molecular docking

We are still facing a Covid-19 pandemic these days and after the aggressively infection control measures taken by the governments in the whole world, there is a need of a rapid pharmaceutical solution in order to control this crisis. The computer aided chemistry and molecular docking is a rapid tool for drug screening and investigation. Moreover, more metal-based drugs are tested daily by research institutes for their antiviral activity. Here, we make use of theoretical studies on previously published biological active complex molecules of vanadium as an example of evaluating possible drug candidates before entering the laboratory. We used DFT calculation studies for structural elucidation and optimization of the molecules and molecular docking studies on several Covid-19 related proteins. Our findings suggest that drug discovery should always be computer -aided. Additionally, it is found that Vtocdea and VXn molecules are seem to be good candidates for further studies as antiviral agents.

Identification of Zika Virus Inhibitors Using Homology Modeling and Similarity-Based Screening to Target Glycoprotein E

The World Health Organization has designated Zika virus (ZIKV) as a dangerous, mosquito-borne pathogen that can cause severe developmental defects. The primary goal of this work was identification of small molecules as potential ZIKV inhibitors that target the viral envelope glycoprotein (ZIKV E) involved in membrane fusion and viral entry. A homology model of ZIKV E containing the small molecule β-octyl glucoside (BOG) was constructed, on the basis of an analogous X-ray structure from dengue virus, and >4 million commercially available compounds were computationally screened using the program DOCK6. A key feature of the screen involved the use of similarity-based scoring to identify inhibitor candidates that make similar interaction energy patterns (molecular footprints) as the BOG reference. Fifty-three prioritized compounds underwent experimental testing using cytotoxicity, cell viability, and tissue culture infectious dose 50% (TCID50) assays. Encouragingly, relative to a known control (NITD008), six compounds were active in both the cell viability assay and the TCID50 infectivity assay, and they showed activity in a third caspase activity assay. In particular, compounds 8 and 15 (tested at 25 μM) and compound 43 (tested at 10 μM) appeared to provide significant protection to infected cells, indicative of anti-ZIKV activity. Overall, the study highlights how similarity-based scoring can be leveraged to computationally identify potential ZIKV E inhibitors that mimic a known reference (in this case BOG), and the experimentally verified hits provide a strong starting point for further refinement and optimization efforts.

In Silico Strategies in Tuberculosis Drug Discovery

Tuberculosis (TB) remains a serious threat to global public health, responsible for an estimated 1.5 million mortalities in 2018. While there are available therapeutics for this infection, slow-acting drugs, poor patient compliance, drug toxicity, and drug resistance require the discovery of novel TB drugs. Discovering new and more potent antibiotics that target novel TB protein targets is an attractive strategy towards controlling the global TB epidemic. In silico strategies can be applied at multiple stages of the drug discovery paradigm to expedite the identification of novel anti-TB therapeutics. In this paper, we discuss the current TB treatment, emergence of drug resistance, and the effective application of computational tools to the different stages of TB drug discovery when combined with traditional biochemical methods. We will also highlight the strengths and points of improvement in in silico TB drug discovery research, as well as possible future perspectives in this field.

Synthesis and antimicrobial evaluation of piperic acid amides and their lower homologues

Seven piperic acid amides along with their lower homologs (12) were synthesized using HATU-DIPEA coupling reagent. All the synthesized derivatives were evaluated for their antibacterial activities against Staphylococcus aureus, Pseudomonas aeruginosa, and vancomycin-resistant P. aeruginosa. They were found to be more active on P. aeruginosa than on S. aureus. However, they did not exhibit potent activity on Vancomycin resistant P. aeruginosa. Among the tested compounds, methylenedioxycinnamic acid amide of anthranilic acid (MDCA-AA, 2a) was found to be most active against S. aureus with MIC of 3.125 μg/ml. The PAS and INH amides of piperic acid were screened against Mycobacterium tuberculosis H37R_a strain. They were found to be most active among all the tested compounds but were found to be less active than the standard drug, isoniazid.

Network Pharmacology-Based Prediction of Active Ingredients and Mechanisms of Lamiophlomis rotata (Benth.) Kudo Against Rheumatoid Arthritis

Background: Lamiophlomis rotata (LR) showed favorable clinical effect and safety on rheumatoid arthritis (RA), but its active ingredients and mechanisms against RA remain unknown. The aim of this work was to explore the active ingredients and mechanisms of LR against RA by network pharmacology. Methods: Compounds from LR were identified using literature retrieval and screened by absorption, distribution, metabolism, excretion, and toxicity (ADMET) evaluation. Genes related to the selected compounds or RA were identified using public databases, and the overlapping genes between compounds and RA target genes were identified using Venn diagram. Then, the interactions network between compounds and overlapping genes was constructed, visualized, and analyzed by Cytoscape software. Finally, pathway enrichment analysis of overlapping genes was carried out on Database for Annotation, Visualization, and Integrated Discovery (DAVID) platform. Results: A total of 148 compounds in LR were identified, and ADMET screen results indicated that 67 compounds exhibited good potential as active ingredients. A total of 90 compounds-related genes and 1,871 RA-related genes were identified using public databases, and 48 overlapping genes between them were identified. Cytoscape results suggested that the active ingredients and target genes of LR against RA consisted of 23 compounds and 48 genes, and luteolin and AKT1 were the uppermost active ingredient and hub gene, respectively. DAVID results exhibited that the mechanisms of LR against RA were related to 34 signaling pathways, and the key mechanism of LR against RA might be to induce apoptosis of synovial cells by inactivating PI3K-Akt signaling pathway. Conclusion: The active ingredients and mechanisms of LR against RA were firstly investigated using network pharmacology. This work provides scientific evidence to support the clinical effect of LR on RA, and a research basis for further expounding the active ingredients and mechanisms of LR against RA.

A drug-likeness toolbox facilitates ADMET study in drug discovery

Undesirable pharmacokinetic (PK) properties or unacceptable toxicity are the main causes of the failure of drug candidates at the clinical trial stage. Since the concept of drug-likeness was first proposed, it has become an important consideration in the selection of compounds with desirable bioavailability during the early phases of drug discovery. Over the past decade, online resources have effectively facilitated drug-likeness studies in an economical and time-efficient manner. Here, we provide a comprehensive summary and comparison of current accessible online resources, in terms of their key features, application fields, and performance for in silico drug-likeness studies. We hope that the assembled toolbox will provide useful guidance to facilitate future in silico drug-likeness research.

A Free Web-Based Protocol to Assist Structure-Based Virtual Screening Experiments

Chemical biology and drug discovery are complex and costly processes. In silico screening approaches play a key role in the identification and optimization of original bioactive molecules and increase the performance of modern chemical biology and drug discovery endeavors. Here, we describe a free web-based protocol dedicated to small-molecule virtual screening that includes three major steps: ADME-Tox filtering (via the web service FAF-Drugs4), docking-based virtual screening (via the web service MTiOpenScreen), and molecular mechanics optimization (via the web service AMMOS2 [Automatic Molecular Mechanics Optimization for in silico Screening]). The online tools FAF-Drugs4, MTiOpenScreen, and AMMOS2 are implemented in the freely accessible RPBS (Ressource Parisienne en Bioinformatique Structurale) platform. The proposed protocol allows users to screen thousands of small molecules and to download the top 1500 docked molecules that can be further processed online. Users can then decide to purchase a small list of compounds for in vitro validation. To demonstrate the potential of this online-based protocol, we performed virtual screening experiments of 4574 approved drugs against three cancer targets. The results were analyzed in the light of published drugs that have already been repositioned on these targets. We show that our protocol is able to identify active drugs within the top-ranked compounds. The web-based protocol is user-friendly and can successfully guide the identification of new promising molecules for chemical biology and drug discovery purposes.

High Impact: The Role of Promiscuous Binding Sites in Polypharmacology

The literature focuses on drug promiscuity, which is a drug’s ability to bind to several targets, because it plays an essential role in polypharmacology. However, little work has been completed regarding binding site promiscuity, even though its properties are now recognized among the key factors that impact drug promiscuity. Here, we quantified and characterized the promiscuity of druggable binding sites from protein-ligand complexes in the high quality Mother Of All Databases while using statistical methods. Most of the sites (80%) exhibited promiscuity, irrespective of the protein class. Nearly half were highly promiscuous and able to interact with various types of ligands. The corresponding pockets were rather large and hydrophobic, with high sulfur atom and aliphatic residue frequencies, but few side chain atoms. Consequently, their interacting ligands can be large, rigid, and weakly hydrophilic. The selective sites that interacted with one ligand type presented less favorable pocket properties for establishing ligand contacts. Thus, their ligands were highly adaptable, small, and hydrophilic. In the dataset, the promiscuity of the site rather than the drug mainly explains the multiple interactions between the drug and target, as most ligand types are dedicated to one site. This underlines the essential contribution of binding site promiscuity to drug promiscuity between different protein classes.

Promising New Antifungal Treatment Targeting Chorismate Synthase from Paracoccidioides brasiliensis

Paracoccidioidomycosis (PCM), caused by Paracoccidioides, is a systemic mycosis with granulomatous character and a restricted therapeutic arsenal. The aim of this work was to search for new alternatives to treat largely neglected tropical mycosis, such as PCM. In this context, the enzymes of the shikimate pathway constitute excellent drug targets for conferring selective toxicity because this pathway is absent in humans but essential for the fungus. In this work, we have used a homology model of the chorismate synthase (EC 4.2.3.5) from Paracoccidioides brasiliensis (PbCS) and performed a combination of virtual screening and molecular dynamics testing to identify new potential inhibitors. The best hit, CP1, successfully adhered to pharmacological criteria (adsorption, distribution, metabolism, excretion, and toxicity) and was therefore used in in vitro experiments. Here we demonstrate that CP1 binds with a dissociation constant of 64 ± 1 μM to recombinant chorismate synthase from P. brasiliensis and inhibits enzymatic activity, with a 50% inhibitory concentration (IC₅₀) of 47 ± 5 μM. As expected, CP1 showed no toxicity in three cell lines. On the other hand, CP1 reduced the fungal burden in lungs from treated mice, similar to itraconazole. In addition, histopathological analysis showed that animals treated with CP1 displayed less lung tissue infiltration, fewer yeast cells, and large areas with preserved architecture. Therefore, CP1 was able to control PCM in mice with a lower inflammatory response and is thus a promising candidate and lead structure for the development of drugs useful in PCM treatment.

Molecular Docking Studies of Phenolic Compounds from Syzygium cumini with Multiple Targets of Type 2 Diabetes

Treatment of type 2 diabetes without any side effects is still a challenge to the medical system. This leads to increasing demand for natural products with antidiabetic activity with fewer side effects. Syzygium cumini is a traditional herbal medicinal plant and is reported to possess a variety of pharmacological actions. It contains various types of chemical constituents including terpenoids, tannins, anthocyanins, flavonoids and other phenolic compounds. Some flavonoids and other phenolic compounds from S. cumini were reported in literature to have type 2 antidiabetic potential. The main objective of the current investigation was in silico screening of some phenolic compounds from S. cumini against multiple targets associated with type 2 diabetes to explore the mechanism of antidiabetic action and prediction of binding mode using molecular docking studies. In silico docking studies were performed for the selected molecules in the binding site of multiple targets associated with type 2 diabetes (α-glucosidas , dipeptidyl peptidase 4, glycogen synthase kinase 3, glucokinase and glucagon receptor). Amongst the compounds tested in silico, rutin showed appreciable binding with multiple targets of type 2 diabetes including α-glucosidase, dipeptidyl peptidase 4, glycogen synthase kinase 3, and glucagon receptor. Catechin was found to inhibit both α-glucosidase, and dipeptidyl peptidase 4. This information can be utilized for the design and development of potent multi-functional candidate drugs with minimal side effects for type 2 diabetes therapeuticsa.

Evolution of In Silico Strategies for Protein-Protein Interaction Drug Discovery

The advent of advanced molecular modeling software, big data analytics, and high-speed processing units has led to the exponential evolution of modern drug discovery and better insights into complex biological processes and disease networks. This has progressively steered current research interests to understanding protein-protein interaction (PPI) systems that are related to a number of relevant diseases, such as cancer, neurological illnesses, metabolic disorders, etc. However, targeting PPIs are challenging due to their "undruggable" binding interfaces. In this review, we focus on the current obstacles that impede PPI drug discovery, and how recent discoveries and advances in in silico approaches can alleviate these barriers to expedite the search for potential leads, as shown in several exemplary studies. We will also discuss about currently available information on PPI compounds and systems, along with their usefulness in molecular modeling. Finally, we conclude by presenting the limits of in silico application in drug discovery and offer a perspective in the field of computer-aided PPI drug discovery.

Fragment-Based Phenotypic Lead Discovery: Cell-Based Assay to Target Leishmaniasis

A rapid and practical approach for the discovery of new chemical matter for targeting pathogens and diseases is described. Fragment-based phenotypic lead discovery (FPLD) combines aspects of traditional fragment-based lead discovery (FBLD), which involves the screening of small-molecule fragment libraries to target specific proteins, with phenotypic lead discovery (PLD), which typically involves the screening of drug-like compounds in cell-based assays. To enable FPLD, a diverse library of fragments was first designed, assembled, and curated. This library of soluble, low-molecular-weight compounds was then pooled to expedite screening. Axenic cultures of Leishmania promastigotes were screened, and single hits were then tested for leishmanicidal activity against intracellular amastigote forms in infected murine bone-marrow-derived macrophages without evidence of toxicity toward mammalian cells. These studies demonstrate that FPLD can be a rapid and effective means to discover hits that can serve as leads for further medicinal chemistry purposes or as tool compounds for identifying known or novel targets.

Molecular dynamics simulation (MDS) analysis of Vibrio cholerae ToxT virulence factor complexed with docked potential inhibitors

The ToxT transcription factor mediates the transcription of the two major virulence factors in Vibrio cholerae. It has a DNA binding domain made of α4, α5, α6, α7, α8, α9 and α10 helices that is responsible for the transcription of virulence genes. Therefore, it is of interest to screen ToxT against the ZINC ligand database containing data for a million compounds. The QSAR model identified 40 top hits for ToxT. Two target protein complexes with ligands Lig N1 and Lig N2 with high score were selected for molecular dynamics simulation. Simulation data shows that ligands are stable in the DNA binding domain of ToxT. Moreover, Lig N1 and Lig N2 passed pharmacological as well as ADME filters along with g-mmpbsa analysis with binding affinity of -199.831 kJ/mol for Lig N1 and - 286.951 kJ/mol for Lig N2. Moreover, no Lipinski and PhysChem violations were identified. It is further observed that these compounds were not inhibitors of P-glycoprotein, CYP450 and renal organic cation transporters. The LD50 of 2.5804 mol/kg for Lig N1 and 2.7788 mol/kg for Lig N2 was noted with acceptable toxicity index.

Blockade of the malignant phenotype by β-subunit selective noncovalent inhibition of immuno- and constitutive proteasomes

A structure-based virtual screening of over 400,000 small molecules against the constitutive proteasome activity followed by in vitro assays led to the discovery of a family of proteasome inhibitors with a sulfonyl piperazine scaffold. Some members of this family of small non-peptidic inhibitors were found to act selectively on the β2 trypsin-like catalytic site with a preference for the immunoproteasome β2i over the constitutive proteasome β2c, while some act on the β5 site and post-acid site β1 of both, the immunoproteasome and the constitutive proteasome. Anti-proliferative and anti-invasive effects on tumor cells were investigated and observed for two compounds. We report novel chemical inhibitors able to interfere with the three types of active centers of both, the immuno- and constitutive proteasomes. Identifying and analyzing a novel scaffold with decorations able to shift the binders’ active site selectivity is essential to design a future generation of proteasome inhibitors able to distinguish the immunoproteasome from the constitutive proteasome.

A combination of docking and cheminformatics approaches for the identification of inhibitors against 4′ phosphopantetheinyl transferase ofMycobacterium tuberculosis

We integrated virtual screening,in vitroandex vivoapproaches to identify numerous potent inhibitory scaffolds againstM. tbPptT.

Searching for potential mTOR inhibitors: Ligand-based drug design, docking and molecular dynamics studies of rapamycin binding site

The PI3K/Akt/mTOR pathway is an important intracellular signaling pathway in cell cycle regulation and its dysregulation is associated with various types of diseases. mTOR (mechanistic or mammalian target of rapamycin) is the main enzyme that performs intermediate control of the signaling pathway through a phosphotransfer process. The classical inhibition of the mTOR pathway is effected by rapamycin and its analogous blocking allosterically the catalytic phosphorylation site, avoiding the deleterious side effects induced by ATP-competitive inhibitors. We employed ligand-based drug design strategies such as pharmacophore searching and analysis, molecular docking, absorption, distribution, metabolism, excretion and toxicity (ADMETox) properties filtering, and molecular dynamics to select potential molecules to become non-ATP competitive inhibitors of the mTOR complex. According to our findings, we propose eight novel potential mTOR inhibitors with similar or better properties than the classic inhibitor complex, rapamycin.

Identification of insulin-sensitizing molecules acting by disrupting the interaction between the Insulin Receptor and Grb14

Metabolic diseases are characterized by a decreased action of insulin. During the course of the disease, usual treatments frequently fail and patients are finally submitted to insulinotherapy. There is thus a need for innovative therapeutic strategies to improve insulin action. Growth factor receptor-bound protein 14 (Grb14) is a molecular adapter that specifically binds to the activated insulin receptor (IR) and inhibits its tyrosine kinase activity. Molecules disrupting Grb14-IR binding are therefore potential insulin-sensitizing agents. We used Structure-Based Virtual Ligand Screening to generate a list of 1000 molecules predicted to hinder Grb14-IR binding. Using an acellular bioluminescence resonance energy transfer (BRET) assay, we identified, out of these 1000 molecules, 3 compounds that inhibited Grb14-IR interaction. Their inhibitory effect on insulin-induced Grb14-IR interaction was confirmed in co-immunoprecipitation experiments. The more efficient molecule (C8) was further characterized. C8 increased downstream Ras-Raf and PI3-kinase insulin signaling, as shown by BRET experiments in living cells. Moreover, C8 regulated the expression of insulin target genes in mouse primary hepatocytes. These results indicate that C8, by reducing Grb14-IR interaction, increases insulin signalling. The use of C8 as a lead compound should allow for the development of new molecules of potential therapeutic interest for the treatment of diabetes.

Statistical Profiling of One Promiscuous Protein Binding Site: Illustrated by Urokinase Catalytic Domain

While recent literature focuses on drug promiscuity, the characterization of promiscuous binding sites (ability to bind several ligands) remains to be explored. Here, we present a proteochemometric modeling approach to analyze diverse ligands and corresponding multiple binding sub-pockets associated with one promiscuous binding site to characterize protein-ligand recognition. We analyze both geometrical and physicochemical profile correspondences. This approach was applied to examine the well-studied druggable urokinase catalytic domain inhibitor binding site, which results in a large number of complex structures bound to various ligands. This approach emphasizes the importance of jointly characterizing pocket and ligand spaces to explore the impact of ligand diversity on sub-pocket properties and to establish their main profile correspondences. This work supports an interest in mining available 3D holo structures associated with a promiscuous binding site to explore its main protein-ligand recognition tendency.

Virtual Screening, pharmacophore development and structure based similarity search to identify inhibitors against IdeR, a transcription factor of Mycobacterium tuberculosis

ideR, an essential gene of Mycobacterium tuberculosis, is an attractive drug target as its conditional knockout displayed attenuated growth phenotype in vitro and in vivo. To the best of our knowledge, no inhibitors of IdeR are identified. We carried out virtual screening of NCI database against the IdeR DNA binding domain followed by inhibition studies using EMSA. Nine compounds exhibited potent inhibition with NSC 281033 (I-20) and NSC 12453 (I-42) exhibiting IC₅₀ values of 2 µg/ml and 1 µg/ml, respectively. We then attempted to optimize the leads firstly by structure based similarity search resulting in a class of inhibitors based on I-42 containing benzene sulfonic acid, 4-hydroxy-3-[(2-hydroxy-1-naphthalenyl) azo] scaffold with 4 molecules exhibiting IC₅₀ ≤ 10 µg/ml. Secondly, optimization included development of energy based pharmacophore and screening of ZINC database followed by docking studies, yielding a molecule with IC₅₀ of 60 µg/ml. More importantly, a five-point pharmacophore model provided insight into the features essential for IdeR inhibition. Five molecules with promising IC₅₀ values also inhibited M. tuberculosis growth in broth culture with MIC₉₀ ranging from 17.5 µg/ml to 100 µg/ml and negligible cytotoxicity in various cell lines. We believe our work opens up avenues for further optimization studies.

Prediction of Anti-Alzheimer's Activity of Flavonoids Targeting Acetylcholinesterase in silico

INTRODUCTION

Prenylated and pyrano-flavonoids of the genus Artocarpus J. R. Forster & G. Forster are well known for their acetylcholinesterase (AChE) inhibitory, anti-cholinergic, anti-inflammatory, anti-microbial, anti-oxidant, anti-proliferative and tyrosinase inhibitory activities. Some of these compounds have also been shown to be effective against Alzheimer's disease.

OBJECTIVE

The aim of the in silico study was to establish protocols to predict the most effective flavonoid from prenylated and pyrano-flavonoid classes for AChE inhibition linking to the potential treatment of Alzheimer's disease.

METHODOLOGY

Three flavonoids isolated from Artocarpus anisophyllus Miq. were selected for the study. With these compounds, Lipinski filter, ADME/Tox screening, molecular docking and quantitative structure-activity relationship (QSAR) were performed in silico. In vitro activity was evaluated by bioactivity staining based on the Ellman's method.

RESULTS

In the Lipinski filter and ADME/Tox screening, all test compounds produced positive results, but in the target fishing, only one flavonoid could successfully target AChE. Molecular docking was performed on this flavonoid, and this compound gained the score as -13.5762. From the QSAR analysis the IC₅₀ was found to be 1659.59 nM. Again, 100 derivatives were generated from the parent compound and docking was performed. The derivative compound 20 was the best scorer, i.e. -31.6392 and IC₅₀ was predicted as 6.025 nM.

CONCLUSION

Results indicated that flavonoids could be efficient inhibitors of AChE and thus, could be useful in the management of Alzheimer's disease. Copyright © 2017 John Wiley & Sons, Ltd.

Design, synthesis and antitubercular potency of 4-hydroxyquinolin-2(1H)-ones

In this study, a 50-membered library of substituted 4-hydroxyquinolin-2(1H)-ones and two closely related analogues was designed, scored in-silico for drug likeness and subsequently synthesized. Thirteen derivatives, all sharing a common 3-phenyl substituent showed minimal inhibitory concentrations against Mycobacterium tuberculosis H37Ra below 10 μM and against Mycobacterium bovis AN5A below 15 μM but were inactive against faster growing mycobacterial species. None of these selected derivatives showed significant acute toxicity against MRC-5 cells or early signs of genotoxicity in the Vitotox™ assay at the active concentration range. The structure activity study relation provided some insight in the further favourable substitution pattern at the 4-hydroxyquinolin-2(1H)-one scaffold and finally 6-fluoro-4-hydroxy-3-phenylquinolin-2(1H)-one (38) was selected as the most promising member of the library with a MIC of 3.2 μM and a CC₅₀ against MRC-5 of 67.4 μM.

Competitive rational inhibitor design to 4-maleylaceto-acetate isomerase

Tyrosinemia type I is the result of genetic disorder in fomaryl acetoacetase gene that leads to 4-fumaryl acetoacetate accumulation. The current treatment for tyrosinemia type I is nitisinone that inhibits 4-hydroxyphenyl pyruvic dioxygenase in competitive manner. In the present study, we have designed two theoretical chemicals, which could inhibit the direct enzyme responsible for fumarylacetoacetate formation. Subset 2_p.0.5 from Zinc database was screened by PyRx software using a Lamarckian genetic algorithm as the scoring function for docking. Top nine successive hits were selected for further pharmacological analysis and finally the new designed ligands RD6-2 (3Z)- 1,3-Butadiene-1,1,2,4-tetrol and RD-7-1 ((Z)-3-[4-Hydroxy-1-(hydroxymethyl)cyclohexyl]-2-propene-1,2-diol could pass PhysChem, FAFDrugs and AdmetSAR filter. The designed ligands were non-substrate and non-inhibitor of CYP450 and nontoxic in AMES test. LD50 of RD-6-2 was 793mg/kg with the toxicity class of four and The LD50 of RD-7-1 was calculated as 5000mg/kg within the toxicity class of five. The designed molecules are introduced as the new theoretical small molecules, which can theoretically inhibit 4- maleylacetoacetate isomerase in a competitive manner.

Virtual screening following rational drug design based approach for introducing new anti amyloid beta aggregation agent

Amyloid β (Aβ) sheets aggregations is the main reason of Alzheimer disease. The interacting areas between monomers are residue number 38 to 42. Inhibition of interaction between Aβ molecules prevents plaque formation. In the present study, we have performed a high-throughput virtual screening among ZINC database and top 1000 hits were checked again regarding binding affinity by AutoDock software. Top 4 successive second step screening hits was considered for drug design purpose against aggregation site of Aβ molecules. The toxicity and pharmacological properties of new designed ligands was assessed by PROTOX and FAFdrugs3 webservers. Several steps of modifications performed in the structures of hit#1 and hit#2 and finally new designed ligand based on hit 1, 1-RD-3 (3-[(Z)-6-Hydroxy-4-{[5-(2-methoxyethyl)-6-methyltetrahydro-2H-pyran-2-yl]methyl}-1-methyl-3-hexenyloxy]tetrahydro-2Hpyran- 4-ol) and a designed ligand based on hit 2, 2-RD-2 (6-(Hydroxymethyl)-4-{5-hydroxy-6-methyl-4-[(3- methylcyclohexyl)methyl]tetrahydro-2H-pyran-2-yloxy}tetrahydro-2H-pyran-2,3,5-triol) could successfully pass pharmacological filters. The LD50 of 37000 mg/kg for 1-RD-3 and 2000 mg/kg for 2-RD-2 indicates that the designed ligands can be considered as new candidates for anti Aβ aggregation to treat Alzheimer's disease. Interestingly, after performing several modification steps still a considerable binding affinity of -9.3 kcal/mol for 1-RD-3 and -9.8 kcal/mol for 2-RD-2 still remained. Theoretically, the new designed molecules can reduce the deposition of Aβ in the cerebral cortex and as the results the Alzheimer symptoms could be decreased.