PHARMACEUTICALS · Published 2026-07-23 · DOI 10.3390/ph19081142
Yini Xie, Runqing Jia, Shuo Chen, Fen Li, Guohui Sun
<b>Background:</b> 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. <b>Methods</b>: 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. <b>Results</b>: 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 <i>R</i><sup>2</sup> = 0.8281, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>Q</mi></mrow><mrow><mi mathvariant="normal">L</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">O</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mo>=</mo></mrow></semantics></math></inline-formula> 0.7653, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi mathvariant="normal">t</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">s</mi><mi mathvariant="normal">t</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mo>=</mo></mrow></semantics></math></inline-formula> 0.8713, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>Q</mi></mrow><mrow><mi mathvariant="normal">F</mi><mi mathvariant="normal">n</mi></mrow><mrow><mn>2</mn></mrow></msubsup><mo>=</mo></mrow></semantics></math></inline-formula> 0.8594 ∼ 0.8837, <i>CCC</i><sub>test</sub> = 0.9301, <i>MAE</i><sub>test</sub> = 0.1966. Similarly, the best cytotoxicity model achieved <i>R</i><sup>2</sup>= 0.8263, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>Q</mi></mrow><mrow><mi mathvariant="normal">L</mi><mi mathvariant="normal">O</mi><mi mathvariant="normal">O</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> = 0.7422, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>R</mi></mrow><mrow><mi mathvariant="normal">t</mi><mi mathvariant="normal">e</mi><mi mathvariant="normal">s</mi><mi mathvariant="normal">t</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> = 0.8951, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msubsup><mrow><mi>Q</mi></mrow><mrow><mi mathvariant="normal">F</mi><mi mathvariant="normal">n</mi></mrow><mrow><mn>2</mn></mrow></msubsup></mrow></semantics></math></inline-formula> = 0.8108~0.8530, <i>CCC</i><sub>test</sub> = 0.9081, <i>MAE</i><sub>test</sub> = 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. <b>Conclusions</b>: 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.
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Xie, Y., Jia, R., Chen, S., et al. (2026). 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. PHARMACEUTICALS. https://doi.org/10.3390/ph19081142