Introduction: Within the scope of the project, this study aimed to find novel inhibitors by combining computational methods. In order to design inhibitors, it was aimed to produce molecules similar to the RdRp inhibitor drug Favipiravir by using the deep learning method.
Methods: For this purpose, a Trained Neural Network (TNN) was used to produce 75 molecules similar to Favipiravir by using Simplified Molecular Input Line Entry System (SMILES) representations. The binding properties of molecules to Viral RNA-dependent RNA polymerase (RdRp) were studied by using molecular docking studies. To confirm the accuracy of this method, compounds were also tested against 3CL protease (3CLpro), which is another important enzyme for the progression of SARS-CoV-2. Compounds having better binding energies and RMSD values than favipiravir were searched with similarity analysis on the ChEMBL drug database in order to find similar structures with RdRp and 3CLpro inhibitory activities.
Results: A similarity search found new 200 potential RdRp and 3CLpro inhibitors structurally similar to produced molecules, and these compounds were again evaluated for their receptor interactions with molecular docking studies. Compounds showed better interaction with RdRp protease than 3CLpro. This result presented that artificial intelligence correctly produced structures similar to favipiravir that act more specifically as RdRp inhibitors. In addition, Lipinski's rules were applied to the molecules that showed the best interaction with RdRp, and 7 compounds were determined to be potential drug candidates. Among these compounds, a Molecular Dynamic simulation study was applied for ChEMBL ID:1193133 to better understand the existence and duration of the compound in the receptor site.
Conclusion: The results confirmed that the ChEMBL ID:1193133 compound showed good Root Mean Square Deviation (RMSD), Root Mean Square Fluctuation (RMSF), hydrogen bonding, and remaining time in the active site; therefore, it was considered that it could be active against the virus. This compound was also tested for antiviral activity, and it was determined that it did not delay viral infection, although it was cytotoxic between 5mg/mL-1.25mg/mL concentrations. However, if other compounds could be tested, it might provide a chance to obtain activity, and compounds should also be tested against the enzymes as well as the other types of viruses.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.2174/0115734064265609231026063624 | DOI Listing |
<b>Background and Objective:</b> Todolo coffee (<i>Coffea arabica</i> L. var. typica) is the oldest commercially grown coffee in the Toraja region of South Sulawesi and is currently at risk of extinction.
View Article and Find Full Text PDFDrug Test Anal
December 2024
Center for Preventive Doping Research, Institute of Biochemistry, German Sport University Cologne, Cologne, Germany.
The 17th edition of the annual banned-substance review on analytical approaches in human sports drug testing is dedicated to literature published between October 2023 and September 2024. As in previous years, focus is put particularly on new or enhanced analytical options in human doping controls as well as investigations into the metabolism and elimination of compounds of interest, which represent central (while not exclusive) cornerstones of the global anti-doping mission. New information published within the past 12 months on established doping agents as well as new potentially relevant substances are reviewed and discussed in the context of the World Anti-Doping Agency's 2024 Prohibited List.
View Article and Find Full Text PDFNat Comput Sci
December 2024
Department of Materials Science and Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA.
Machine learning plays an important role in quantum chemistry, providing fast-to-evaluate predictive models for various properties of molecules; however, most existing machine learning models for molecular electronic properties use density functional theory (DFT) databases as ground truth in training, and their prediction accuracy cannot surpass that of DFT. In this work we developed a unified machine learning method for electronic structures of organic molecules using the gold-standard CCSD(T) calculations as training data. Tested on hydrocarbon molecules, our model outperforms DFT with several widely used hybrid and double-hybrid functionals in terms of both computational cost and prediction accuracy of various quantum chemical properties.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Microbiology and Immunology, McGill University, Montreal, QC, Canada.
Continued efforts to discover new antibacterial molecules are critical to achieve a robust pre-clinical pipeline for new antibiotics. Screening of compound or natural product extract libraries remains a widespread approach and can benefit from the development of whole cell assays that are robust, simple and versatile, and allow for high throughput testing of antibacterial activity. In this study, we created and validated two bioluminescent reporter strains for high-throughput screening, one in Pseudomonas aeruginosa, and another in a hyperporinated and efflux-deficient Escherichia coli.
View Article and Find Full Text PDFSci Rep
December 2024
Department of Chemical Engineering, RVR & JC College of Engineering (A), Guntur, Andhra Pradesh, 522019, India.
The study analyzed the aqueous leaf extracts of Moringa oleifera and Musa sps. for phytochemical components, including flavonoids, sterols, saponins, tannins, and glycosides. The LC-MS analysis revealed gingerol, vicenin-2, caffeic acid, quercetin, and other compounds in the extracts.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!