TRIAGE: An AI Scientist for Adversarial Target Falsification

Despite massive investment in drug discovery, more than 90\% of drug candidates fail in clinical trials, often because the underlying target hypothesis proves ineffective or unsafe in humans. We present TRIAGE (Target Review via Iterative Adversarial Generation and Evaluation), a multi-agent framework that shifts target discovery from hypothesis generation to hypothesis falsification. TRIAGE combines a generative agent for target nomination with two adversarial agents for evidence retrieval and $\textit{in silico}$ perturbation analyses. By systematically challenging proposed targets, TRIAGE aims to filter out false positives before they advance into the costly downstream stages of drug development. We evaluate TRIAGE using benchmarks derived from historical clinical trial outcomes and public biomedical databases.