Unraveling the Mechanism of Drug Binding to SARS-CoV-2 RNA Pseudoknot with Thermodynamics-Driven Machine Learning
arXiv:2604.14906v3 Announce Type: replace-cross Abstract: The pseudoknot secondary structure in SARS-CoV-2 RNA is essential for regulating protein synthesis through $-$1 programmed ribosomal frameshifting ($-1$ PRF), a mechanism that allows the virus to generate both structural and non-structural proteins from overlapping reading frames. This pseudoknot exhibits both threaded and unthreaded long-lived topologies. The influence of ligand binding on its folding is a process critical for the development of $-$1 PRF small-molecule inhibitors. Understanding this process through unbiased molecular d
The continuous evolution of AI and machine learning techniques, combined with sustained research into SARS-CoV-2 and other viruses, enables more sophisticated analysis of fundamental biological mechanisms.
This research provides a deeper understanding of viral function and offers a novel, AI-driven pathway for developing targeted antiviral therapies, moving beyond traditional drug discovery methods.
The application of thermodynamics-driven machine learning significantly enhances the precision and speed of identifying potential drug candidates that interfere with viral replication mechanisms.
- · Biopharmaceutical industry
- · AI/ML in drug discovery sector
- · Viral therapeutics researchers
- · Public health
- · Viral pathogens
Machine learning accelerates the identification and optimization of small-molecule inhibitors for viral targets.
This methodology could be generalized to other viral infections or diseases involving RNA structures, broadening the scope of AI-driven drug development.
Reduced burden from infectious diseases through faster and more effective drug development leads to improved global health and economic stability.
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