IBM MAMMAL Foundation Model Unifies Gene and Protein Analysis

On May 14, 2026, IBM Research unveiled MAMMAL, a unified foundation model designed to integrate multiple biological modalities into a single computational framework. The release addresses the workflow fragmentation typical in computational biology by processing genes, proteins, and small molecules as part of the same computational language.

Unified Multi-Modal Architecture

The flagship model, ibm/biomed.omics.bl.sm.ma-ted-458m, is a 458-million parameter transformer. The architecture introduces a novel multi-align method to process disparate biological data types simultaneously. A core technical component is the Modular Tokenizer, which translates diverse biological inputs into a shared multi-dimensional space.

This shared tokenization enables the model to handle cross-modal tasks, such as predicting how a specific small molecule drug interacts with a specific gene-expression profile. IBM supports these workflows with a structured multi-domain prompt syntax. Researchers can combine different biological entities as inputs and outputs for classification, regression, and generation tasks. The model achieved its baseline capabilities through pretraining on over 2 billion biological samples, including protein sequences, antibody data, small molecule chemistry, and single-cell gene-expression profiles.

Benchmark Results and AlphaFold Comparison

IBM evaluated MAMMAL across 11 diverse drug discovery benchmarks, achieving state-of-the-art performance on nine and maintaining competitive scores on the remaining two.

MAMMAL targets a different segment of the bioinformatics pipeline than structural prediction models. While tools like AlphaFold 3 focus heavily on 3D structural folding, MAMMAL is optimized for biology-in-context tasks like drug-target interaction and transcriptomic lab test predictions. In direct specific tests for antibody-antigen binding, fine-tuned MAMMAL prediction scores outperformed AlphaFold 3 confidence-score proxies on five of seven antigen targets.

Feature	MAMMAL	AlphaFold 3
Core Optimization	Biology-in-context interactions	3D structural prediction
Antigen Target Outperformance	5 of 7 targets	2 of 7 targets
Task Capabilities	Cross-modal classification and regression	Structural rendering and folding

Hardware Requirements and Deployment

The framework is available under the open-source Apache 2.0 license. The implementation codebase is hosted on GitHub in the BiomedSciAI/biomed-multi-alignment repository, while the pretrained weights are available on Hugging Face under the IBM biomedical model family.

Running the 458M model requires minimal overhead compared to large language models. You need at least 16GB of VRAM for baseline inference tasks. Fine-tuning the model for custom interaction datasets typically requires 40GB or more of VRAM to maintain efficient batch sizes.

If you build computational biology platforms, MAMMAL provides an infrastructure layer that can replace distinct sequence and molecule models with a unified multi-modal prompt. Deploy this model to run high-throughput filtering and narrow down candidate interactions before committing financial resources to wet-lab validation.