TESSERA combines LLMs as local policy and evaluator with MCTS on knowledge graphs to compose mechanistic drug-disease explanations.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Starling uses LLMs and agents to turn 22.5M PubMed papers into 6.3M nuanced structured records across six tasks with 0.6-7.7% frontier-model rejection rates, lower than error rates on existing curated databases.
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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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.