Target context boosts performance in scarce-data regimes when fused properly via FiLM but degrades results under distribution shift, while standard molecular benchmarks suffer from severe leakage and trivial baselines.
Derek van Tilborg, Alisa Alenicheva, and Francesca Grisoni
3 Pith papers cite this work. Polarity classification is still indexing.
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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.
On LIT-PCBA, supervised ML re-ranking reaches median EF1% of 4.49 while AutoDock-GNINA scores 2.14 and consensus does not exceed the best single scorer.
citing papers explorer
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When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction
Target context boosts performance in scarce-data regimes when fused properly via FiLM but degrades results under distribution shift, while standard molecular benchmarks suffer from severe leakage and trivial baselines.
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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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Benchmarking Single-Pose Docking, Consensus Rescoring, and Supervised ML on the LIT-PCBA Library: A Critical Evaluation of DiffDock, AutoDock-GPU, GNINA, and DiffDock-NMDN
On LIT-PCBA, supervised ML re-ranking reaches median EF1% of 4.49 while AutoDock-GNINA scores 2.14 and consensus does not exceed the best single scorer.