AI-Driven Discovery of Allosteric Modulators for Non-Canonical HCV Targets

Most current efforts, including those by Qu et al. (2025), use machine learning to optimize inhibitors for well-studied HCV proteins (NS3, NS5A, NS5B). This idea takes it a step further: use AI-augmented molecular docking and dynamics to predict allosteric sites (i.e., regulatory pockets outside of active sites) on less-studied HCV proteins such as core protein or p7 ion channel. By focusing on allosteric modulation, which can be less prone to resistance, and on “non-canonical” targets, this approach could yield first-in-class antivirals. Pairing AI modeling with high-throughput screening of compound libraries—including natural products like curcumin analogs (Sharma et al., 2024)—could dramatically expand the range of HCV therapies, especially for resistant or atypical genotypes.

References:

1. Structural bioinformatics approaches for predicting novel drug targets in hepatitis C virus proteins: a comprehensive analysis. Miao Qu, Mingzhu Gao, Xisheng Sang, Miao Yu, Zihe Guan, Weizhi Chang (2025). Scientific Reports.
2. Promising Potential of Curcumin and Related Compounds for Antiviral Drug Discovery.. Archana Sharma, Twinkle Sharma, R. Bhaskar, M. Ola, Alok Sharma, Vijay Kumar Thakur, P. C. Sharma (2024). Medicinal chemistry.