Constraint-Aware Corrective Memory for Language-Based Drug Discovery Agents
Large language models are making autonomous drug discovery agents increasingly feasible, but reliable success in this setting is not determined by any single action or molecule. It is determined by whether the final returned set jointly satisfies protocol-level requirements such as set size, diversity, binding quality, and developability. This creates a fundamental control problem: the agent plans step by step, while task validity is decided at the level of the whole candidate set. Existing language-based drug discovery systems therefore tend to rely on long raw history and under-specified self-reflection, making failure localization imprecise and planner-facing agent states increasingly ...
| Authors | Maochen Sun, Youzhi Zhang, Gaofeng Meng |
|---|---|
| Primary category | cs.AI |
| Published | 10 April 2026 |
| Last updated at source | 10 April 2026 |
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