Ensembits creates a discrete vocabulary for protein conformational ensembles that outperforms static tokenizers on dynamics prediction tasks and enables ensemble token prediction from single structures via distillation.
Boltz-2: Towards Accurate and Efficient Binding Affinity Prediction
5 Pith papers cite this work, alongside 390 external citations. Polarity classification is still indexing.
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ProtDBench standardizes protein binder design evaluation using wet-lab data, exposing verifier biases, metric dependencies, and trade-offs between success rate, speed, and structural diversity.
HADES is an agentic AI system that generates mechanistic hypotheses for drug-induced liver injury using molecular, metabolite, and pathway evidence, outperforming prior binary classifiers on the new DILER benchmark while establishing a baseline for hypothesis alignment.
SPADE selects ligands more efficiently than deep learning or Bayesian optimization, needing fewer tests on average to identify high-quality drug candidates for novel proteins.
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.
citing papers explorer
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ENSEMBITS: an alphabet of protein conformational ensembles
Ensembits creates a discrete vocabulary for protein conformational ensembles that outperforms static tokenizers on dynamics prediction tasks and enables ensemble token prediction from single structures via distillation.
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ProtDBench: A Unified Benchmark of Protein Binder Design and Evaluation
ProtDBench standardizes protein binder design evaluation using wet-lab data, exposing verifier biases, metric dependencies, and trade-offs between success rate, speed, and structural diversity.
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An explainable hypothesis-driven approach to Drug-Induced Liver Injury with HADES
HADES is an agentic AI system that generates mechanistic hypotheses for drug-induced liver injury using molecular, metabolite, and pathway evidence, outperforming prior binary classifiers on the new DILER benchmark while establishing a baseline for hypothesis alignment.
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SPADE: Faster Drug Discovery by Learning from Sparse Data
SPADE selects ligands more efficiently than deep learning or Bayesian optimization, needing fewer tests on average to identify high-quality drug candidates for novel proteins.
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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.