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.