Better predictive models built together
Drug discovery is slow and expensive, but in-silico predictions help speed it up. Better predictions need more data. Unfortunately, this is often practically impossible due to intellectual property challenges for sharing data. Let’s change this together!
We help you build federated data networks to enable hospitals, fellow researchers and other pharmaceutical innovators to build the best predictive models possible. We enable you to train and use significantly better AI models while preserving data privacy and security.
Use Federated Data Networks for Any Case of Computational Drug Research
Federated data networks provide computational scientists with extended data reach, ensuring secure, large-scale collaboration without losing control over their proprietary data:
Protein Structure Prediction
Improve structure-based drug design by training AI models on distributed 3D molecular structural data and sequence data (RNA & DNA)
Molecular Property Prediction
Access a broad spectrum of molecular representations (SMILES, InChI, molecular fingerprints) to enhance predictions of solubility, permeability, and pharmacokinetics across diverse chemical spaces.
ADME and Toxicity Profiling
In-silico ADMET models benefit from exposure to more diverse experimental assay data (e.g., hERG inhibition, CYP450 interactions), increasing the reliability of toxicity and drug-likeness predictions while minimizing late-stage failures.
Binding Affinity Prediction
Secure access to distributed high-throughput screening (HTS), surface plasmon resonance (SPR), and isothermal titration calorimetry (ITC) data enhances machine learning models for small-molecule interactions with protein targets.
Generative Drug Design
Federated learning enables the training of deep generative models (e.g., variational autoencoders, reinforcement learning frameworks) on proprietary compound libraries while preserving intellectual property, accelerating novel molecule generation.
Powering the AISB Network to Revolutionize AI Drug Discovery
Apheris provides the tech layer for the Artificial Intelligence Structural Biology (AISB) Network, an unprecedented collaboration among AbbVie, Astex, AstraZeneca, 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.
The Benefits of Federated Data Networks
By leveraging federated data networks, computational scientists can:
Improve predictive accuracy in lead optimization
Increased data diversity results in more generalizable and reliable models.
Reduce experimental costs and accelerate discovery
AI-driven insights reduce reliance on costly and time-consuming wet-lab experiments.
Enhance translational research
Federated models that integrate clinical and preclinical datasets help bridge the gap between discovery and patient outcomes.
Ensure regulatory and security compliance
Data remains within institutional firewalls, supporting compliance with GDPR, HIPAA, and other regulatory frameworks. Stay in full control of your data while training the best models possible.
Proprietary data is the new differentiator
We are seeing AI models in drug discovery becoming increasingly commoditized, the real differentiator is increasingly access to high-quality, diverse data. The sheer scale and diversity of chemical space means that many models still struggle with applicability and accuracy. Apheris is changing that, making it possible to unlock valuable, industrial discovery data while preserving privacy and IP. The potential for impact across the industry is enormous
The next step for computational drug discovery
Federated data networks provide computational scientists with access to richer datasets while maintaining security and data sovereignty. This collaborative framework enhances predictive modeling, molecular design, and therapeutic discovery, creating a new standard for computational drug discovery.
The future of AI-driven science is decentralized, collaborative, and more powerful than ever.