Limited training data and a lack of diversity limit the performance and applicability domain of ADMET prediction models.
Predicting ADMET endpoints is challenging due to:
Process complexity
Low-throughput experimental assays
Limited chemically relevant data, especially in early-stage research
Only few pharmaceutical and biotech companies have large and diverse enough proprietary datasets to train robust QSAR models of sufficient quality.
The solution
Biopharmaceutical consortium for collaborative training of ADMET models while protecting data confidentiality.
Gain access to orders of magnitude more and more diverse data through collaboration.
Apheris enables pharma & biotech companies to train ADMET models on their proprietary data without the need for data sharing.
Join the ADMET consortium to collaboratively train higher-quality ADMET models
The ADMET consortium will offer you three key benefits:
Achieve state-of-the-art ADMET prediction models through collaborative federated learning
Expand the applicability domain and accuracy of these models beyond current state-of-the-art
Enable members to customize consortium models to their proprietary chemical spaces
Learn more about the consortium setup, the datasets contributed, and the models used in the ADMET consortium
Powering the AISB Consortium to Revolutionize AI Drug Discovery
Apheris provides the tech layer for the Artificial Intelligence Structural Biology (AISB) Consortium, an unprecedented collaboration among AbbVie, Boehringer Ingelheim, Johnson & Johnson, Sanofi and Takeda aimed at transforming AI drug discovery. State-of-the-art AI models will be trained and evaluated on unique data from multiple biopharma companies without exposing proprietary information.