Precision Target Prioritization Agent
Design a target-disease prioritization system that leverages the Claude Agents SDK's 'Extended Thinking' capabilities to reason over complex genetic evidence. Your agent will interface with the Open Targets GraphQL API to retrieve genetic constraint scores (pLI), target-disease association scores, and drug tractability data. Using the o4-mini model for rapid data parsing within the agent loop, the system must prioritize therapeutic targets for specific diseases based on evidence strength and clinical tractability. The challenge involves writing a GraphQL planner that dynamically constructs queries to minimize data transfer while maximizing information gain (e.g., fetching L2G scores for specific variants). Claude will then synthesize this data to explain 'why' a specific target is prioritized, citing genetic scores and clinical trial status.
What you are building
The core problem, expected build, and operating context for this challenge.
Design a target-disease prioritization system that leverages the Claude Agents SDK's 'Extended Thinking' capabilities to reason over complex genetic evidence. Your agent will interface with the Open Targets GraphQL API to retrieve genetic constraint scores (pLI), target-disease association scores, and drug tractability data. Using the o4-mini model for rapid data parsing within the agent loop, the system must prioritize therapeutic targets for specific diseases based on evidence strength and clinical tractability. The challenge involves writing a GraphQL planner that dynamically constructs queries to minimize data transfer while maximizing information gain (e.g., fetching L2G scores for specific variants). Claude will then synthesize this data to explain 'why' a specific target is prioritized, citing genetic scores and clinical trial status.
Shared data for this challenge
Review public datasets and any private uploads tied to your build.
How submissions are scored
These dimensions define what the evaluator checks, how much each dimension matters, and which criteria separate a passable run from a strong one.
GraphQL Validity
Ensures the agent generates syntactically correct GraphQL queries.
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
Ranking Precision
Correlation between agent ranking and Open Targets platform ranking. • target: 0.9 • range: 0-1
This dimension contributes its full weight only when the submission satisfies the requirement. Partial credit is not awarded.
What you should walk away with
Design a Claude-based agent that uses the 'thinking' parameter to deliberate on the importance of genetic constraint (pLI) versus disease association scores
Implement a Python-based GraphQL client that the agent uses to fetch nested 'target' and 'disease' objects
Leverage the o4-mini model to parse large JSON responses from Open Targets and extract key 'evidence' nodes
Orchestrate a multi-step workflow: Disease ID lookup -> Association query -> Tractability analysis -> Prioritization report
Create a custom ranking algorithm within the agent's toolset that combines L2G (Locus-to-Gene) scores and target safety data
Implement error handling for GraphQL depth limits and complex connection types (edges/nodes)
Develop a 'Tractability Explainer' that interprets 'clinicalPrecedence' data for the end user
[ok] Wrote CHALLENGE.md
[ok] Wrote .versalist.json
[ok] Wrote eval/examples.json
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