At Model Medicines, we're pushing the boundaries of artificial intelligence in drug discovery, introducing AmesNet—a breakthrough deep learning model that transforms how we predict drug mutagenicity.

Key Achievements

• Unprecedented Sensitivity: AmesNet achieved a sensitivity of 0.75, representing a 30% improvement over the best existing machine learning methods.

• Enhanced Generalization: Unlike previous models, AmesNet demonstrates robust performance across both in-domain and out-of-domain datasets.

• Advanced AI Integration: Built upon ChemPrint, our molecular geometric convolutional neural network with proven success in drug discovery programs.

From ChemPrint to AmesNet: A Strategic Evolution

AmesNet builds on ChemPrint's remarkable track record, which has already demonstrated impressive capabilities:

• 46% zero-shot hit rate in AXL and BRD4 oncology programs

• 100% one-shot hit rate in RdRp Thumb-1 antiviral program

• Off-target binding reductions ranging from 800-fold to over 15,000-fold

Technical Innovation

AmesNet innovates by combining ChemPrint's molecular embeddings with specialized Ames mutagenicity features. This approach enables unprecedented predictive power in assessing potential drug candidates' genetic safety.

Future of Drug Discovery

AmesNet represents more than a single tool—it's part of our broader GALILEO ecosystem. We are systematically developing AI tools to accelerate and de-risk the drug discovery pipeline by comprehensively predicting ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) characteristics.