Computational Evaluation of Phytochemicals as Potential Anti-HIV Drugs Targeting CCR5 and CXCR4 Receptors
Nebir, S. S.; Al Arian, T.; Sarkar, B.; Moni, R.; Malek, S.; Zohora, U. S.; Rahman, M. S.
Show abstract
HIV is a major worldwide health concern; hence new therapeutic approaches are needed to fight viral resistance and enhance treatment results. HIV entrance into host cells depends on the CCR5 and CXCR4 receptors, which makes them potential targets for antiviral medication development. The objective of this study is to computationally evaluate 53 phytochemicals that target CCR5 and CXCR4 as potential anti-HIV medications. Effective anti-HIV medications were projected to be phytochemicals that may inhibit these receptors and so interfere with the HIV life cycle. AutoDock Vina was used to perform the molecular docking investigation from which six phytochemicals capable of inhibiting CCR5 and CXCR4 were identified based on the lowest docking score. i.e., Withaferin A, Oleanolic Acid, Ursolic Acid, Theaflavine, Camptothecin, and Hypericin. The SWISSADME server was utilized to decide their druglikeness properties, the ADMETlab server to predict different pharmacokinetic and pharmacodynamic properties, the PASS-Way2Drug server to evaluate their activity spectra, and the RS-WebPredictor server to figure out the metabolism in the body. They adhered to Lipinskis rule of five and had promising ADME/toxicity study result along with favorable molecular dynamics simulation. Overall, the above-mentioned six phytochemicals might have the potential to be used as alternative HIV therapeutics.
Matching journals
The top 7 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Identification of Natural Antiviral Drug Candidates Against Tilapia Lake Virus: Computational Drug Design Approaches 98%
- Deep learning based predictive modeling to screen natural compounds against TNF-alpha for the potential management of Rheumatoid Arthritis: Virtual screening to comprehensive in silico investigation 97%
- Molecular docking, simulation and binding free energy analysis of small molecules as PfHT1 inhibitors 97%
Similar papers in this journal
- Pharmacophore modeling, 2D-QSAR, Molecular Docking and ADME studies for the discovery of inhibitors of PBP2a in MRSA 97%
- Molecular Elucidation of Pancreatic Elastase Inhibition by Baicalein 96%
- Structural analysis and ensemble docking revealed the binding modes of selected progesterone receptor modulators 96%
Similar papers in this journal
- Elucidation of Structural Mechanism of ATP Inhibition at the AAA1 Subunit of Cytoplasmic Dynein 1 Using a Chemical "Toolkit" 96%
- Possible link between higher transmissibility of B.1.617 and B.1.1.7 variants of SARS-CoV-2 and increased structural stability of its spike protein and hACE2 affinity 95%
- Effect of Delta and Omicron mutations on the RBD-SD1 do-main of the Spike protein in SARS-CoV-2 and the Omicron mutations on RBD-ACE2 interface complex 94%
Similar papers in this journal
- Mechanistic insights into the Japanese Encephalitis Virus RNA dependent RNA polymerase protein inhibition by bioflavonoids from Azadirachta indica 96%
- Machine learning prediction of antiviral-HPV protein interactions for anti-HPV pharmacotherapy 95%
- Novel Peptide Inhibitor of Human Tumor Necrosis Factor-α has Antiarthritic Activity 95%
Similar papers in this journal
- Utilizing Heteroatom Types and Numbers from Extensive Ligand Libraries to Develop Novel hERG Blocker QSAR Models Using Machine Learning-based Classifiers 97%
- Molecular Glue-Design-Evaluator (MOLDE): An Advanced Method for In-Silico Molecular Glue Design 96%
- Chalcogen derivatives for the treatment of African trypanosomiasis: biological evaluation of thio and seleno- semicarbazones and their azole derivatives 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.