Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
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An integrated framework with ConGA-PepPI for PepPI prediction and binding-site localization plus TC-PepGen for target-conditioned peptide generation reports 0.839 accuracy and 0.921 AUROC in cross-validation along with 40.39% of generated peptides exceeding native templates on AlphaFold 3 ipTM.
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Benchmarking open-source tools for in silico antiviral drug discovery
Boltz-2 and fine-tuned DrugFormDTA lead ML-based binding prediction while GNINA leads docking tools on a cleaned antiviral dataset, with performance varying by viral protein.
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An Integrated Deep-Learning Framework for Peptide-Protein Interaction Prediction and Target-Conditioned Peptide Generation with ConGA-PepPI and TC-PepGen
An integrated framework with ConGA-PepPI for PepPI prediction and binding-site localization plus TC-PepGen for target-conditioned peptide generation reports 0.839 accuracy and 0.921 AUROC in cross-validation along with 40.39% of generated peptides exceeding native templates on AlphaFold 3 ipTM.