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Join the Absorption, Distribution, Metabolism, Excretion, Toxicity (ADMET) Network
Improve the accuracy and applicability of ADMET models through collaborative training on proprietary life sciences data - while preserving IP protection
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Beyond the data-volume assumption: The role of complementary data in ADMET modelling
Improved ADMET predictions come from complementary chemistry, not sheer data volume. A scientific study of public–proprietary integration shows why diversity, balance, and harmonization matter for reliability, calibration, and broader model applicability.
Federated Learning for ADMET Prediction: Expanding Model Applicability
This blog explores state-of-the-art science in ADMET prediction, showing how federated learning enables pharma companies to collaboratively train models on diverse data, achieving higher accuracy and broader applicability without compromising data privacy.
Fine-tuning OpenFold3 on a small set of structures: The PDE10A case study
We fine-tuned OpenFold3 on just 10 PDE10A protein–ligand complexes and evaluated on 17 held-out structures. Even this low-n setup corrected systematic pose errors and improved interface metrics, making predictions more usable for design decisions.
