MultiAgentSystemInitialization
Participants will submit their code, a detailed report on the multi-agent system architecture, the iterative optimization process, and the top 10 optimized lead compounds with their predicted properties. Evaluation will prioritize the robustness of the multi-agent framework, the novelty and quality of the generated compounds, and the predictive accuracy of the integrated models.
Evaluation overview
How the linked challenge is judged: tasks, benchmarks, and criteria count.
Task templates
Inputs and expected outputs.
MultiAgentSystemInitialization
Initialize the CAMEL multi-agent system with defined agents and roles for drug optimization.
JSON with 'target_protein_id': string, 'initial_lead_SMILES': string.
JSON with 'agents_initialized': boolean, 'initial_dialogue_summary': string.
LeadOptimizationIteration
Execute one iteration of the multi-agent lead optimization process, generating new compounds and predicting properties.
JSON with 'current_lead_SMILES': string, 'iteration_number': integer.
JSON array of objects, each with 'SMILES', 'binding_affinity_score', 'ADMET_profile': dictionary.
PineconeIntegrationAndSearch
Store optimized lead compounds in Pinecone and perform a similarity search.
JSON array of objects, each with 'SMILES', 'embedding': array of floats, 'properties': dictionary, 'query_SMILES': string for similarity search.
JSON array of objects, each with 'SMILES', 'score': float (similarity).