We are excited to announce that the transformative capabilities of IBM's MAMMAL (Molecular Aligned Multi-Modal Architecture and Language) are now available for free on curecancerwithai.com through our public API. This cutting-edge biomedical foundation model has been developed by IBM Research and represents a significant advancement in drug discovery and cancer research.

What is MAMMAL?

MAMMAL is a multi-modal biomedical foundation model designed to process and analyze various biological data types, including proteins, small molecules, and single-cell gene/omics data. The term "multi-modal" refers to the model's ability to understand and integrate information from different biological modalities within a unified framework. This capability is particularly important in cancer and drug-discovery research, as it allows scientists to explore complex biological interactions and make more informed predictions.

The Research Behind MAMMAL

The MAMMAL model is detailed in a comprehensive study published in npj Drug Discovery (Nature). In this groundbreaking paper, the authors describe how MAMMAL was trained on approximately 2 billion biological samples, covering a wide range of data types. The model was evaluated on 11 diverse drug-discovery tasks and achieved state-of-the-art results on 9 of them, showcasing its effectiveness and reliability in the field.

MAMMAL in the Cure Cancer With AI API

Cure Cancer With AI is thrilled to offer three powerful MAMMAL API endpoints that cater to different aspects of biomedical research. All endpoints are designed to be user-friendly and return valuable predictions based on the input data.

• Protein–Protein Interaction Prediction (POST /api/v1/mammal/ppi): This endpoint allows users to input two amino-acid sequences (protein_a and protein_b). The model predicts whether the two proteins interact, returning a binding-affinity class label of "1" for interacting or "0" for non-interacting. This information is crucial for understanding biological pathways and disease mechanisms.
• Drug–Target Interaction (POST /api/v1/mammal/dti): Users can submit a target protein amino-acid sequence (target_seq) along with a drug represented in SMILES notation (drug_seq). The model provides a predicted pKd value (−log₁₀ Kd), indicating the strength of the predicted binding between the drug and the target protein. A higher pKd suggests a stronger predicted interaction, which is essential for drug development and therapeutic strategies.
• ClinTox Clinical-Trial Toxicity Prediction (POST /api/v1/mammal/clintox): This endpoint takes a compound in SMILES notation (smiles) and returns a toxicity prediction. The model classifies the compound as either toxic/likely to fail trials (pred=1) or not toxic (pred=0), along with a raw toxicity score. This information is vital for assessing the safety of drug candidates during clinical trials.

All of these MAMMAL endpoints are available for free on curecancerwithai.com. To get started, you can create a free API key at /api-keys and explore the comprehensive documentation, including parameters and code samples, at /developers. The free tier allows up to 100 requests per hour per key, making it accessible for researchers and developers alike.

Start Using It for Free

Take advantage of the free MAMMAL inference capabilities now available on curecancerwithai.com. Simply create your free API key at /api-keys, and dive into the full documentation at /developers. With 100 requests per hour available at no cost, you have the perfect opportunity to enhance your research with state-of-the-art predictions.

Conclusion

We are proud to offer the powerful MAMMAL inference capabilities for free on curecancerwithai.com. This multi-modal biomedical foundation model has the potential to revolutionize drug discovery and cancer research, providing valuable insights into protein interactions, drug-target binding, and clinical trial toxicity. Remember, the predictions generated by this API are research signals and should not be considered medical or clinical advice.

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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.