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DrugDiscoveryBench

Science · 2026-06-30

DrugDiscoveryBench evaluates coding agents on 82 expert-curated, verifiable multi-step tasks spanning target identification and validation, hit identification, hit-to-lead analysis, and lead optimization in an adapted Biomni tool environment. The primary metric averages the pass rates of three independent runs per model setting; a task passes only when its final-answer outcome score is 100. The 82-task count is the distinct task cohort, not a single-run binomial denominator for these averaged scores.

Top models (higher is better)

ModelScore
GPT-6 Astra68.7
Gemini 4 Argon65.4
Muse Spark 1.362.2
DeepSeek V4.1 Flash 101760.6
GPT-5.6 Sol56.5
Claude Opus 553.7
GPT-5.551.6
Sonnet 550.0
Gemini 3.5 Flash50.0
Opus 4.846.8
Gemini 3.1 Pro Preview41.9
GLM-5.236.2
Kimi K2.7 Code35.3
DeepSeek-V4-Pro31.7
Sonnet 4.631.3
GPT-5.229.3
Qwen3.7-Max (source unspecified)29.3
Opus 4.627.7
MiniMax M322.8
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