DrugSAGE:Self-evolving Agent Experience for Efficient State-of-the-Art Drug Discovery
The article discusses a new framework called DrugSAGE, designed to enhance drug discovery through self-evolving agent experiences. This framework allows for the accumulation and reuse of knowledge across various tasks, significantly improving efficiency in developing state-of-the-art predictive models. The results indicate that DrugSAGE outperforms existing agents in both single-task and cross-task evaluations.
- ▪DrugSAGE accumulates experience across tasks to improve drug discovery efficiency.
- ▪It maintains a memory of verified skills and effective strategies, reducing the need for extensive trial and error.
- ▪In evaluations, DrugSAGE achieved an average score of 0.935, outperforming baseline agents by 10-30%.
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Computer Science > Machine Learning arXiv:2605.15461 (cs) [Submitted on 14 May 2026] Title:DrugSAGE:Self-evolving Agent Experience for Efficient State-of-the-Art Drug Discovery Authors:Yikun Zhang, Xiwei Cheng, Tianyu Liu, Yuanqi Du, Wengong Jin View a PDF of the paper titled DrugSAGE:Self-evolving Agent Experience for Efficient State-of-the-Art Drug Discovery, by Yikun Zhang and 4 other authors View PDF HTML (experimental) Abstract:Building state-of-the-art (SOTA) predictive models for drug discovery requires expensive search over tools, architectures, and training strategies. Current LLM-based agents can find SOTA solutions through extensive trial and error, but they do not retain the experience accumulated along the way and therefore pay the full search cost on every new task.
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