We are excited to announce that Cure Cancer With AI now offers free access to the IBM MAMMAL AI model through our public API. MAMMAL, which stands for "Molecular Aligned Multi-Modal Architecture and Language," is a cutting-edge biomedical foundation model developed by IBM Research. This powerful tool is designed to enhance cancer and drug-discovery research by providing insights across multiple biological domains.

So, what exactly does "Molecular Aligned Multi-Modal Architecture and Language" mean? In simple terms, MAMMAL is a multi-modal model that integrates information from proteins, small molecules, and gene/omics data into a unified framework. This allows researchers to analyze and make predictions about complex biological interactions and drug responses, which are crucial for advancing cancer treatments.

The Research Behind MAMMAL

The capabilities of MAMMAL are detailed in the research paper published in npj Drug Discovery (Nature). The model has been trained on approximately 2 billion biological samples and evaluated on 11 diverse drug-discovery tasks. Impressively, MAMMAL achieved new state-of-the-art results on 9 of these tasks, while performing comparably on the remaining two. This remarkable performance underscores the significance of MAMMAL as a valuable resource for researchers seeking to enhance their understanding of drug interactions and biological processes.

MAMMAL in the Cure Cancer With AI API

With the introduction of MAMMAL into our API, we are providing researchers with three powerful endpoints, each designed to facilitate different aspects of biomedical research:

• Protein–Protein Interaction (PPI) Prediction: - Endpoint: /api/v1/mammal/ppi - What it does: This endpoint takes two amino-acid sequences (representing two proteins) as input and predicts whether they interact or not. - Input Format: JSON body containing protein_a and protein_b. - Output: Returns a binding-affinity class label of "1" (indicating interaction) or "0" (indicating non-interaction).
• Drug–Target Interaction (DTI) Prediction: - Endpoint: /api/v1/mammal/dti - What it does: This endpoint predicts the interaction between a target protein and a drug. - Input Format: JSON body with target_seq (the target protein's amino-acid sequence) and drug_seq (the drug's SMILES notation). - Output: Returns a predicted pKd value (−log₁₀ Kd), where a higher value indicates a stronger predicted binding.
• ClinTox Clinical-Trial Toxicity Prediction: - Endpoint: /api/v1/mammal/clintox - What it does: This endpoint assesses the toxicity of a compound based on its structure. - Input Format: JSON body containing smiles (the compound's SMILES notation). - Output: Returns a toxicity prediction (1 for toxic/likely to fail trials, 0 for not toxic) along with a raw toxicity score.

These MAMMAL endpoints are now available to researchers and developers at no cost. Each API key allows for up to 100 requests per hour, making it easy to integrate MAMMAL's capabilities into your projects.

Start Using It for Free

Getting started is simple! You can create a free API key by visiting /api-keys. Once you have your key, you can explore the full documentation, including parameters and code samples, by heading over to /developers. Remember, this is a fantastic opportunity to leverage state-of-the-art AI for your research in drug discovery and cancer treatment.

Conclusion

We are thrilled to provide free access to the IBM MAMMAL AI model through the Cure Cancer With AI API. This is an invaluable resource for researchers looking to explore the complexities of drug interactions and protein functions in the quest for better cancer treatments. Please note that while MAMMAL provides experimental predictions, these outputs should not be considered medical or clinical advice. They serve as research signals only, intended to aid in your investigations.

Take advantage of this opportunity and start using MAMMAL today at curecancerwithai.com!

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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.