We are thrilled to announce that the revolutionary IBM MAMMAL AI model is now accessible for free through the Cure Cancer With AI public API. Developed by IBM Research, MAMMAL stands for "Molecular Aligned Multi-Modal Architecture and Language," a cutting-edge biomedical foundation model that integrates various biological data types to enhance cancer and drug-discovery research.

What is MAMMAL and Why Does it Matter?

MAMMAL is a multi-modal biomedical foundation model that harnesses the power of artificial intelligence to analyze a broad spectrum of data, including proteins, small molecules, and single-cell gene/omics data. This model is significant because it allows researchers to gain insights from multiple data types within a unified framework, ultimately accelerating the discovery of new therapeutic targets and drug candidates in the fight against cancer.

The multi-modal approach means that MAMMAL can process and understand different types of biological information simultaneously. By representing proteins, small molecules, and transcriptomic data within the same sequence framework, MAMMAL can perform complex tasks such as classification, regression, and generation—essential for advancing biomedical research.

Research Behind MAMMAL

The capabilities of MAMMAL have been rigorously evaluated in the research paper titled "MAMMAL — Molecular Aligned Multi-Modal Architecture and Language for biomedical discovery", published in npj Drug Discovery (Nature). This comprehensive study utilized MAMMAL to analyze approximately 2 billion biological samples across multiple modalities and tested it on 11 diverse drug-discovery tasks. MAMMAL achieved new state-of-the-art results on 9 of these tasks, demonstrating its effectiveness and reliability in the realm of biomedical discovery.

MAMMAL in the Cure Cancer With AI API

We are excited to announce that Cure Cancer With AI now provides three specific MAMMAL endpoints for researchers and developers looking to leverage this powerful AI model:

• Protein–Protein Interaction Prediction (PPI)
  • Endpoint: POST /api/v1/mammal/ppi
  • Input: Two amino-acid sequences, designated as protein_a and protein_b.
  • Output: A binding-affinity class label of "1" (indicating interaction) or "0" (indicating no interaction).
• Drug–Target Interaction Prediction (DTI)
  • Endpoint: POST /api/v1/mammal/dti
  • Input: A target protein amino-acid sequence (target_seq) and a drug represented in SMILES notation (drug_seq).
  • Output: A predicted pKd value (−log₁₀ Kd; higher values suggest stronger predicted binding).
• ClinTox Clinical-Trial Toxicity Prediction
  • Endpoint: POST /api/v1/mammal/clintox
  • Input: A compound represented in SMILES notation (smiles).
  • Output: A toxicity prediction (pred 1 = toxic/likely to fail trials, 0 = not toxic) along with a raw score.

These endpoints are designed to provide significant insights into protein interactions, drug-target affinities, and potential clinical trial outcomes, all of which are crucial for advancing drug discovery efforts.

Start Using It for Free!

The great news is that all of these MAMMAL capabilities are available for free on Cure Cancer With AI. Anyone interested can create a free API key at /api-keys. With this key, you can make up to 100 requests per hour, which allows ample opportunity to explore the powerful predictions generated by MAMMAL. For full documentation, parameters, and code samples, please visit /developers.

Conclusion

The introduction of MAMMAL AI support through our free public API marks a significant step forward in utilizing advanced machine learning techniques for cancer research. With the ability to predict protein interactions, drug-target affinities, and clinical trial toxicity, researchers now have powerful tools at their fingertips—all available for free on Cure Cancer With AI.

As a reminder, while MAMMAL offers valuable predictions, it is crucial to emphasize that these predictions serve as research signals and do not constitute medical or clinical advice. We encourage users to utilize this resource responsibly and in conjunction with professional guidance.

To dive deeper into practical AI-for-cancer-research updates, explore our latest blog posts, learn more about our mission, and see how you can support ongoing work on our donations page.

Cure Cancer With AI is an educational research and information platform. It does not provide medical advice, diagnosis, or treatment recommendations; always discuss care decisions with a qualified healthcare professional.