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LLM agents struggle with drug design tasks on new SMDD-Bench

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced SMDD-Bench, a new benchmark designed to evaluate the capabilities of large language model agents in small molecule drug design. The benchmark comprises 502 task instances across five types, including scaffold hopping and lead optimization, involving 102 unique protein targets. Even the top-performing model, GPT-5.4, managed to solve only 40.2% of these complex tasks, highlighting the significant challenges that remain in achieving fully autonomous computational drug design. AI

IMPACT Highlights current limitations of LLM agents in complex scientific domains, guiding future research in autonomous drug design.

RANK_REASON The cluster describes a new academic paper introducing a benchmark for evaluating LLM capabilities in a specific scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

LLM agents struggle with drug design tasks on new SMDD-Bench

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The cluster describes a new academic paper introducing a benchmark for evaluating LLM capabilities in a specific scientific domain. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Kevin Han, Renfei Zhang, Kathy Wei, Hamed Mahdavi, Niloofar Mireshghallah, Amir Barati Farimani ·

    SMDD-Bench: Can LLMs Solve Real-World Small Molecule Drug Design Tasks?

    arXiv:2605.21740v2 Announce Type: replace Abstract: LLM agents have incredible potential for scientific discovery applications. However, the performance of LLM agents on real-world, small molecule drug design (SMDD) tasks across diverse chemistries and targets is unclear. Current…

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