DrugTargetWorld: A Synthetic Biobank for Training and Benchmarking AI Scientists
cs.AI
Submitted: 2026-10-07
Updated: 2026-10-07
Comments: 34 pages main text, 92 pages supplementary material; 6 main figures. Project: https://DrugTargetWorld.vercel.app. Code: https://github.com/sammargolis/DrugTargetWorld. Data: https://huggingface.co/datasets/sammargolis/DrugTargetWorld-assets
Code: https://github.com/sammargolis/DrugTargetWorld
Project page: https://drugtargetworld.vercel.app
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Terminology
Sources
- ResidencyRL: Reinforcement Learning in Simulated Clinical Environments
- ScienceAgentBench: Toward Rigorous Assessment of Language Agents for Data-Driven Scientific Discovery
- CausalGame: Benchmarking Causal Thinking of LLM Agents in Games
- SWE-bench: Can Language Models Resolve Real-World GitHub Issues?
- CLadder: Assessing Causal Reasoning in Language Models
- BixBench3: Benchmarking AI agents on research-study-scale computational biology tasks
- CausalDS: Benchmarking Causal Reasoning in Data-Science Agents
- DiscoveryBench: Towards Data-Driven Discovery with Large Language Models
- BixBench: a Comprehensive Benchmark for LLM-based Agents in Computational Biology
- Aviary: training language agents on challenging scientific tasks
- Reinforcement Learning with Verifiable Rewards Implicitly Incentivizes Correct Reasoning in Base LLMs
- CausaLab: A Scalable Environment for Interactive Causal Discovery Toward AI Scientists
- CausalBN-Bench: A Comprehensive Benchmark for Causal Learning Capability of LLMs
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