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Achieving an average binding score of -8.85 kcal/mol, conDitar-dev not only excels in binding affinity but also optimizes for critical drug developability properties, setting a new standard in computational drug design.
LLM agents can learn complex, multi-turn tasks far more effectively by explicitly separating planning from execution, using a hierarchical RL approach with carefully designed credit assignment.
