IBM MAMMAL Beats AlphaFold 3 on 9 Biological Benchmarks with Multi-Modal Biology Model

What Is MAMMAL

MAMMAL (Multi-domain Alignment Multi-Modal and Augmentation Learning) is IBM Research's new multi-modal biology foundation model, published in Nature. Unlike models that specialize in one domain, MAMMAL processes proteins, small molecules, and gene data together, targeting drug discovery and biological understanding.

Benchmark Results

MAMMAL achieves state-of-the-art performance on 9 out of 11 biological benchmarks, surpassing AlphaFold 3 on several tasks. Its strongest areas include:

Drug-target interaction prediction (will a molecule bind to a protein?)

Ligand binding and affinity prediction (how strongly does a drug bind?)

Antibody-antigen interaction prediction

Cell type classification and gene expression prediction

Relationship to AlphaFold 3

The IBM team is careful to position MAMMAL as complementary rather than competitive with AlphaFold 3. AlphaFold 3 excels at protein structure prediction; MAMMAL's strength is multi-domain interaction prediction spanning proteins, molecules, and genes together.

Significance

Multi-modal biology AI can significantly accelerate early-stage drug discovery — candidate identification, binding prediction, and toxicity screening. MAMMAL's results suggest that integrating multiple biological data types into a single model is more powerful than training specialized models on each type in isolation.