predict_dti
Predict drug–target binding affinity as pKd (−log10 Kd; higher = stronger binding) using IBM MAMMAL. Inference is CPU-bound and may take up to ~60s.
This record as markdown: /tools/io-github-hifarrer-cure-cancer-with-ai/predict-dti.md
What predict_dti does on Cure Cancer With AI
AI agents invoke predict_dti to trigger actions in Cure Cancer With AI. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.
| Parameter | Type | Required | Description |
|---|---|---|---|
drug_seq | string | Yes | Drug structure in SMILES notation. |
norm_y_std | number | — | Optional normalization standard-deviation override. |
target_seq | string | Yes | Target protein amino-acid sequence (single-letter codes). |
norm_y_mean | number | — | Optional normalization mean override. |
Parameters from the server's own tool schema.
Why predict_dti is rated High
This tool triggers an external AI/ML inference computation via IBM MAMMAL, making it Execute. It runs a model prediction rather than simply reading stored data. Severity is medium because misuse could waste significant compute resources (up to 60s CPU-bound per call) and could potentially be abused to flood the inference service, but it has no direct data modification or financial consequences.
From the tool's definition 'Predict drug–target binding affinity...using IBM MAMMAL. Inference is CPU-bound and may take up to ~60s.'
Attacks that exploit this kind of access
The rule that runs predict_dti safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Cure Cancer With AI, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For predict_dti, this is the rule to start with:
predict_dti stays usable, but rate-capped: a runaway agent can't fire it dozens of times a minute. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Cure Cancer With AI, apply this rule, and every predict_dti call is checked against it from then on.
Questions about predict_dti
Predict drug–target binding affinity as pKd (−log10 Kd; higher = stronger binding) using IBM MAMMAL. Inference is CPU-bound and may take up to ~60s. It is categorised as a Execute tool in the Cure Cancer With AI MCP Server, which means it can trigger actions or run processes. Use rate limits and argument validation.
predict_dti accepts 4 parameters: drug_seq, norm_y_std, target_seq, norm_y_mean. Required: drug_seq, target_seq. The full parameter table on this page comes from the server's own tool schema.
Register the Cure Cancer With AI MCP server in PolicyLayer and add a rule for predict_dti: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Cure Cancer With AI. Nothing to install.
predict_dti is a Execute tool with high risk. Execute tools should be rate-limited and have argument validation enabled.
Yes. Add a rate_limit block to the predict_dti rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for predict_dti. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
predict_dti is provided by the Cure Cancer With AI MCP server (https://www.curecancerwithai.com/api/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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