VIRTECS: Virtual Screening Of Therapeutic Classes Using Encodings Of Chemical Structures

Author(s):  
Dweepa Honnavalli ◽  
Kavya Varma ◽  
Gowri Srinivasa
2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Musab Mohamed Ibrahim ◽  
Tilal Elsaman ◽  
Mosab Yahya Al-Nour

The design, synthesis, and development of novel non-steroidal anti-inflammatory drugs (NSAIDs) with better activity and lower side effects are respectable area of research. Novel Diclofenac Schiff’s bases (M1, M2, M4, M7, and M8) were designed and synthesized, and their respective chemical structures were deduced using various spectral tools (IR, 1H NMR, 13C NMR, and MS). The compounds were synthesized via Schiff’s condensation reaction and their anti-inflammatory activity was investigated applying the Carrageenan-induced paw edema model against Diclofenac as positive control. Percentage inhibition of edema indicated that all compounds were exhibiting a comparable anti-inflammatory activity as Diclofenac. Moreover, the anti-inflammatory activity was supported via virtual screening using molecular docking study. Interestingly compound M2 showed the highest in vivo activity (61.32% inhibition) when compared to standard Diclofenac (51.36% inhibition) as well as the best binding energy score (-10.765) and the virtual screening docking score (-12.142).


2019 ◽  
Vol 12 (1) ◽  
pp. 20 ◽  
Author(s):  
Ryan Ramos ◽  
Josivan Costa ◽  
Rai Silva ◽  
Glauber da Costa ◽  
Alex Rodrigues ◽  
...  

Aedes aegypti is the main vector of dengue fever transmission, yellow fever, Zika, and chikungunya in tropical and subtropical regions and it is considered to cause health risks to millions of people in the world. In this study, we search to obtain new molecules with insecticidal potential against Ae. aegypti via virtual screening. Pyriproxyfen was chosen as a template compound to search molecules in the database Zinc_Natural_Stock (ZNSt) with structural similarity using ROCS (rapid overlay of chemical structures) and EON (electrostatic similarity) software, and in the final search, the top 100 were selected. Subsequently, in silico pharmacokinetic and toxicological properties were determined resulting in a total of 14 molecules, and these were submitted to the PASS online server for the prediction of biological insecticide and acetylcholinesterase activities, and only two selected molecules followed for the molecular docking study to evaluate the binding free energy and interaction mode. After these procedures were performed, toxicity risk assessment such as LD50 values in mg/kg and toxicity class using the PROTOX online server, were undertaken. Molecule ZINC00001624 presented potential for inhibition for the acetylcholinesterase enzyme (insect and human) with a binding affinity value of −10.5 and −10.3 kcal/mol, respectively. The interaction with the juvenile hormone was −11.4 kcal/mol for the molecule ZINC00001021. Molecules ZINC00001021 and ZINC00001624 had excellent predictions in all the steps of the study and may be indicated as the most promising molecules resulting from the virtual screening of new insecticidal agents.


2009 ◽  
Vol 07 (03) ◽  
pp. 473-497 ◽  
Author(s):  
AARON SMALTER ◽  
JUN HUAN ◽  
GERALD LUSHINGTON

In this paper, we introduce a novel statistical modeling technique for target property prediction, with applications to virtual screening and drug design. In our method, we use graphs to model chemical structures and apply a wavelet analysis of graphs to summarize features capturing graph local topology. We design a novel graph kernel function to utilize the topology features to build predictive models for chemicals via Support Vector Machine classifier. We call the new graph kernel a graph wavelet-alignment kernel. We have evaluated the efficacy of the wavelet-alignment kernel using a set of chemical structure–activity prediction benchmarks. Our results indicate that the use of the kernel function yields performance profiles comparable to, and sometimes exceeding that of the existing state-of-the-art chemical classification approaches. In addition, our results also show that the use of wavelet functions significantly decreases the computational costs for graph kernel computation with more than ten fold speedup.


2014 ◽  
Vol 2014 ◽  
pp. 1-7 ◽  
Author(s):  
Jens Krüger ◽  
Richard Grunzke ◽  
Sonja Herres-Pawlis ◽  
Alexander Hoffmann ◽  
Luis de la Garza ◽  
...  

Virtual high-throughput screening (vHTS) is an invaluable method in modern drug discovery. It permits screening large datasets or databases of chemical structures for those structures binding possibly to a drug target. Virtual screening is typically performed by docking code, which often runs sequentially. Processing of huge vHTS datasets can be parallelized by chunking the data because individual docking runs are independent of each other. The goal of this work is to find an optimal splitting maximizing the speedup while considering overhead and available cores on Distributed Computing Infrastructures (DCIs). We have conducted thorough performance studies accounting not only for the runtime of the docking itself, but also for structure preparation. Performance studies were conducted via the workflow-enabled science gateway MoSGrid (Molecular Simulation Grid). As input we used benchmark datasets for protein kinases. Our performance studies show that docking workflows can be made to scale almost linearly up to 500 concurrent processes distributed even over large DCIs, thus accelerating vHTS campaigns significantly.


2019 ◽  
Vol 15 (3) ◽  
pp. 265-276
Author(s):  
Eram Shakeel ◽  
Rajnish Kumar ◽  
Neha Sharma ◽  
Salman Akhtar ◽  
Mohd. Kalim Ahmad Khan ◽  
...  

<P>Introduction: The regulation of apoptosis via compounds originated from marine organisms signifies a new wave in the field of drug discovery. Marine organisms produce potent compounds as they hold the phenomenal diversity in chemical structures. The main focus of drug development is anticancer therapy. Methods: Expertise on manifold activities of compounds helps in the discovery of their derivatives for preclinical and clinical experiment that promotes improved activity of compounds for cancer patients. Results: These marine derived compounds stimulate apoptosis in cancer cells by targeting Bcl-2 and Survivin, highlighting the fact that instantaneous targeting of these proteins by novel derivatives results in efficacious and selective killing of cancer cells. Conclusion: Our study reports the identification of Aplysin and Haterumaimide J as Bcl-2 inhibitors and Cortistatin A as an inhibitor of survivin protein, from a sequential virtual screening approach.</P>


Author(s):  
Ekampreet Singh ◽  
Rameez Jabeer Khan ◽  
Rajat Kumar Jha ◽  
Gizachew Muluneh Amera ◽  
Monika Jain ◽  
...  

Abstract Background The COVID-19 pandemic caused by SARS-CoV-2 has shown an exponential trend of infected people across the planet. Crediting its virulent nature, it becomes imperative to identify potential therapeutic agents against the deadly virus. The 3-chymotrypsin-like protease (3CLpro) is a cysteine protease which causes the proteolysis of the replicase polyproteins to generate functional proteins, which is a crucial step for viral replication and infection. Computational methods have been applied in recent studies to identify promising inhibitors against 3CLpro to inhibit the viral activity. Main body of the abstract This review provides an overview of promising drug/lead candidates identified so far against 3CLpro through various in silico approaches such as structure-based virtual screening (SBVS), ligand-based virtual screening (LBVS) and drug-repurposing/drug-reprofiling/drug-retasking. Further, the drugs have been classified according to their chemical structures or biological activity into flavonoids, peptides, terpenes, quinolines, nucleoside and nucleotide analogues, protease inhibitors, phenalene and antibiotic derivatives. These are then individually discussed based on the various structural parameters namely estimated free energy of binding (ΔG), key interacting residues, types of intermolecular interactions and structural stability of 3CLpro-ligand complexes obtained from the results of molecular dynamics (MD) simulations. Conclusion The review provides comprehensive information of potential inhibitors identified through several computational methods thus far against 3CLpro from SARS-CoV-2 and provides a better understanding of their interaction patterns and dynamic states of free and ligand-bound 3CLpro structures.


2013 ◽  
Vol 12 (08) ◽  
pp. 1341001 ◽  
Author(s):  
JU BAO ◽  
JIN F. LIU ◽  
XIAO HE ◽  
JOHN Z. H. ZHANG

Fusion of HIV-1 viral and host cellular membranes is an important step for HIV infection. The HIV-1 envelope glycoprotein mediating the membrane fusion consists of subunits gp120 and gp41 whereas gp120 recognizes the cell-surface receptors and gp41 promotes viral-cell membrane fusion. The trimeric helical complex composed of heterodimer of N-terminal and C-terminal extraviral segments has been used for the gp41 function study, and the trimeric N-terminal teptad repeat (NHR) is considered as an antiviral drug target for developing HIV-1 membrane fusion inhibitors. By using computational solvent probe mapping, we have explored druggable sites on the trimeric NHR peptides, and identified residues K574 and R579 as the hot spots for inhibitor designing. We further demonstrated that although NB-2 and NB-64 are all N-substituted Pyrrole derivatives and have very similar chemical structures, it is possible that diverse inhibitory mechanisms targeting different negative electrostatic residues (K574 and R579) exist. Results from fragment-based virtual screening identified series of potential lead compounds which could be used for further design of fusion inhibitors.


Author(s):  
A. M. Andrianov ◽  
Yu. V. Kornoushenko ◽  
A. D. Karpenko ◽  
A. V. Tuzikov

To find small-molecule compounds that can simulate the structural and functional properties of the high affinity X77 ligand of the main protease of SARS-CoV-2 - etiologic agent of COVID-19, the virtual screening of 9 molecular libraries of the Pharmit web server containing over 213.5 million chemical structures was performed. Using molecular modeling, the neutralizing activity of the identified molecules was evaluated, resulting in 5 leader compounds promising for synthesis and testing for antiviral activity. The data obtained indicate that these compounds may be used as basic structures for the development of effective drugs to treat the novel coronavirus infection.


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