HomeShanghai AI Lab Unveils ViraHInter to Boost Antiviral Drug Discovery
Shanghai Artificial Intelligence Lab, in partnership with Fudan University, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, and the Shanghai Institute for Viral Research, has introduced ViraHInter, a novel AI predictive model. This release signifies a major leap in antiviral drug development, enabling the prediction of how viruses hijack human proteins without relying on traditional wet-lab experiments.
ViraHInter leverages dual-modal data from both protein sequences and structures. While conventional methods typically examine either amino acid sequences or 3D structures in isolation, ViraHInter uniquely captures both simultaneously with high precision. The model generates full-atom 3D structures of virus-host protein complexes, mapping atomic relationships to facilitate drug design. Additionally, it employs a protein language model to identify conserved patterns that persist despite rapid viral mutations, significantly enhancing prediction accuracy.
Benchmark tests highlight ViraHInter’s exceptional performance. It achieved a prediction accuracy of 0.50 for virus-human protein interactions, outperforming AlphaFold3 by 4.5 times and surpassing other existing methods. The research team applied the model to three influenza subtypes, successfully identifying 33 shared host factors, which underscores its substantial potential in antiviral drug discovery.
Crucially, ViraHInter demonstrates robust adaptability against newly emerging viruses. Even under strict sequence homology controls, the model maintained high performance, confirming its broad applicability in addressing novel pathogens. These findings offer promising new targets and directions for developing treatments against influenza and coronaviruses.
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Shanghai Artificial Intelligence Lab, in partnership with Fudan University, Ruijin Hospital affiliated to Shanghai Jiao Tong University School of Medicine, and the Shanghai Institute for Viral Research, has introduced ViraHInter, a novel AI predictive model. This release signifies a major leap in antiviral drug development, enabling the prediction of how viruses hijack human proteins without relying on traditional wet-lab experiments.
ViraHInter leverages dual-modal data from both protein sequences and structures. While conventional methods typically examine either amino acid sequences or 3D structures in isolation, ViraHInter uniquely captures both simultaneously with high precision. The model generates full-atom 3D structures of virus-host protein complexes, mapping atomic relationships to facilitate drug design. Additionally, it employs a protein language model to identify conserved patterns that persist despite rapid viral mutations, significantly enhancing prediction accuracy.
Benchmark tests highlight ViraHInter’s exceptional performance. It achieved a prediction accuracy of 0.50 for virus-human protein interactions, outperforming AlphaFold3 by 4.5 times and surpassing other existing methods. The research team applied the model to three influenza subtypes, successfully identifying 33 shared host factors, which underscores its substantial potential in antiviral drug discovery.
Crucially, ViraHInter demonstrates robust adaptability against newly emerging viruses. Even under strict sequence homology controls, the model maintained high performance, confirming its broad applicability in addressing novel pathogens. These findings offer promising new targets and directions for developing treatments against influenza and coronaviruses.
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