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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| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.15461 |
| Publication time | Mon, 18 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-18T04:04:54.418Z |
| Last seen | 2026-05-18T04:04:54.418Z |
| Headline source | Publisher (no WeSearch rewrite) |
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| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | PCwrpEI-nQWQ · 2 stories |
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| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
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| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
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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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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