In Silico Evaluation of Potential SARS-CoV-2 RNA-Dependent RNA Polymerase (RdRp) Inhibitors Derived from Philippine Natural Products

Potential Active Compounds from Cashew (Anacardium occidentale L.) Leaves against SARS-CoV-2 RNA-Dependent RNA Polymerase: An In Silico Study

Coronavirus disease 2019 (COVID-19), caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2), has driven the search for effective antiviral candidates. SARS-CoV-2 RNA replication is catalyzed by RNA-dependent RNA polymerase (RdRp), making this enzyme a potential target for antiviral drug development. Cashew (Anacardium occidentale L.) leaves are traditionally consumed in Southeast Asia, and several of their polyphenolic compounds have been reported to inhibit RNA-dependent RNA polymerases of other RNA viruses. This study aimed to identify potential SARS-CoV-2 RdRp inhibitors through molecular docking and molecular dynamics simulations and to evaluate their drug-likeness and predicted bioactivity. Five polyphenolic compounds formed stable interactions with RdRp, as indicated by RMSD, RMSF, interaction energy, and radius of gyration analyses. These compounds warrant further in vitro and in vivo evaluation as candidate SARS-CoV-2 RdRp inhibitors.

In Silico Identification of Potent SARS-CoV-2 RdRp Inhibitors: DFT and Molecular Docking Analysis of Acylated Glucopyranoside Derivatives

Severe acute respiratory syndrome (SARS) is a global health threat caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), highlighting the urgent need for the development of new therapeutic agents. This study involves a computational investigation of the design and assessment of several methyl α-D-glucopyranoside (MDG) derivatives as potential inhibitors of the SARS-CoV-2 RdRp chain. MDG derivatives 2-5 were optimized through quantum mechanical methods, and their therapeutic potential properties against SARS-CoV-2 was evaluated through a comprehensive in silico approach. Thermodynamic properties and frontier molecular orbital (FMO) studies were utilized to characterize the chemical stability and reactivity. Molecular electrostatic potential (MEP) and natural bond orbital (NBO) charge analyses revealed the reactive electrophilic and nucleophilic sites within the derivatives. Molecular docking was employed to predict the binding affinities and interaction profiles of the SARS-CoV-2 RdRp chain targets (PDB ID: 6M71). Molecular docking simulations indicated that compound 2, with its long aliphatic chains, exhibited the highest binding affinity, with a docking score of -7.1. The strong hydrogen bonding interactions with LYS47 and HIS133 and one hydrophobic contact with ALA130 (5.20 A), a key residue involved in active site accessibility, contributed to the high binding affinity of compound 2. Additionally, LYS47 formed strong hydrogen bonds with the derivative. ADMET predictions suggested that the derivatives generally possess a favorable safety profile, with low genotoxicity and neurotoxicity. Overall, these results demonstrate the promise of modified MDG derivatives as lead compounds and justify further in vitro and in vivo studies to validate their antiviral activity against SARS-CoV-2. The Chittagong Univ. J. Sci. 46(1): 137-163, 2025

Precision repurposing of clinically characterized molecules targeting dengue virus RNA-dependent RNA polymerase: an in silico identification and in vitro validation.

Integrated in silico and in vitro approaches reveal novel coumarin hybrids targeting influenza A virus RNA-dependent RNA polymerase.

Influenza A virus (IAV) remains a significant public health threat due to its high mutation rate and increasing resistance to existing antiviral drugs. The viral RNA-dependent RNA polymerase (RdRp) is essential for viral replication and represents a promising target for new therapeutics. We screened our in-house chemical library of 103 compounds against two domains of the IAV RdRp complex using molecular docking. Based on computational findings, we selected eleven coumarin-phenolic acid hybrids and one thiazolidinone compounds for in vitro evaluation against H1N1 and H5N1. LMed 93 and LMed 99 exhibited potent antiviral activity, with EC50 values of 5.66 and 1.21 μM against H1N1 respectively, and high selectivity indices. Mechanistic studies showed that LMed 99 acts throughout early to late stages of infection, while LMed 93 impacts early and intermediate stages. Both compounds significantly inhibited viral RNA synthesis, with LMed 99 achieving 82% inhibition at 1- and 3-h post-infection (hpi) and 80% at 6 hpi, and LMed 93 achieving 82% (1 hpi), 78% (3 hpi), and 60% (6 hpi). LMed 99 reduced viral proteins and mRNA in a time-dependent manner, indicating interference with viral transcription. A mini-replicon assay showed that LMed 93, at 70 and 35 μM, reduces polymerase activity by 53.7% and 38.7%, while LMed 99 at 9 and 4.5 μM increased expression by 108.97% and 61.37%, respectively. Structure-activity relationship analysis highlighted that catechol groups and halogenation enhance antiviral potency. These findings suggest that LMed 93 and LMed 99 are promising coumarin-based scaffolds for developing new anti-Influenza A agents, also offering potential strategies for combating other RNA viruses.

Computational Identification of Potential RSV L-RdRp Inhibitors with Predicted Superior Activity and Safety Profiles over Remdesivir Using QSAR Modeling, Molecular Docking and Molecular Dynamics Simulations

Background: Respiratory syncytial virus (RSV) RNA-dependent RNA polymerase (RdRp) complex is an essential molecular machine for viral genome replication. The L protein, the catalytic subunit of this complex (L-RdRp), is well-characterized structurally and represents a highly promising target for the development of novel small-molecule drugs against RSV. Methods: To address the limitations of current QSAR-based virtual screening strategies for RSV L-RdRp inhibitor development, we established a multi-dimensional computer-aided drug screening framework integrating activity, toxicity, drug-likeness, and stability. Results: Two OECD-compliant 2D-QSAR models were developed and rigorously validated to predict inhibitory activity and cytotoxicity, respectively. The optimal inhibitory activity model exhibited strong statistical performance, with R2 = 0.8281, QLOO2= 0.7653, Rtest2= 0.8713, QFn2= 0.8594 ∼ 0.8837, CCCtest = 0.9301, MAEtest = 0.1966. Similarly, the best cytotoxicity model achieved R2= 0.8263, QLOO2 = 0.7422, Rtest2 = 0.8951, QFn2 = 0.8108~0.8530, CCCtest = 0.9081, MAEtest = 0.1685. Based on these models, a four-step screening workflow—QSAR-based filtering and molecular docking (15,758 → 2446 → 162 → 19 compounds), ADMET evaluation (19 → 5), and molecular dynamics simulations (MDSs)—was implemented to identify promising L-RdRp inhibitors. Conclusions: Ultimately, five candidate compounds were selected, all of which demonstrated predicted higher inhibitory activity, lower predicted cytotoxicity, a stable predicted binding mode, and favorable oral bioavailability compared with the reference drug remdesivir. These findings provide valuable in silico-derived lead candidates and a reliable computational workflow for identifying experimental L-RdRp inhibitors targeting RSV.

Identification of potential inhibitors of dengue virus ns5 methyltransferase and polymerase domains through virtual screening and molecular dynamics studies

Molecular dynamics simulations showed that the S-adenosylmethionine (SAM)-binding site ligand formed a more stable complex with lower root-mean-square deviation (RMSD) and reduced flexibility, indicating a promising lead candidate for further optimization and experimental validation against dengue virus NS5.