Protein Ligand Binding Affinity Prediction (Structure based AI)
In silico structure-based drug design combined with accurate binding affinity prediction can significantly reduce both time and costs needed for research projects. Via the Apheris Platform for federated and privacy preserving data science, universal scoring functions can be run. Using these functions, multiple companies can jointly train generalizable and interpretable protein-ligand interaction models in an IP-preserving way.
Federated Drug Discovery: Quantitative Structure Activity Relationship (QSAR)
The Apheris Platform for federated and privacy preserving data science enables multiple pharmaceutical partners to jointly train AI models and discover novel molecular structure-property insights (Quantitative Structure-Activity Relationship/QSAR models).
Deep Multi-Task Learning on Heterogenous Drug Discovery Data
Automated in silico drug discovery pipelines guide and speed up the drug discovery and development process of pharma and biotech companies. Commercial biological and medical datasets are typically sparse, and machine learning models that are trained to predict bioactivity do not generalize well. Via the Apheris Platform, multiple companies jointly train multi-task models on heterogenous data of different modalities. The diverse data leads to a better representation of drug compounds and their biological properties. Apheris’ federated machine learning framework enables model training without revealing key secrets of each pharma company and enables faster and more accurate drug discoveries.