PWRules extracts complementary pairing rules between protein words and small molecule fragments from affinity data to predict binding, achieving performance comparable to Glide and PSICHIC while showing structural enrichment near binding pockets without using 3D data in training.
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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 Interpretable Framework Applying Protein Words to Predict Protein-Small Molecule Complementary Pairing Rules
PWRules extracts complementary pairing rules between protein words and small molecule fragments from affinity data to predict binding, achieving performance comparable to Glide and PSICHIC while showing structural enrichment near binding pockets without using 3D data in training.
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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.