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LLMs evaluated for molecular property prediction, showing mixed results on accuracy and uncertainty

By PulseAugur Editorial · [2 sources] ·

Researchers are exploring the use of large language models (LLMs) for molecular property prediction, a field previously dominated by classical methods. A study evaluating seven frontier LLMs found that while they can improve accuracy with sufficient in-context examples, their self-reported uncertainties only weakly correlate with prediction errors. Another paper, which has since been withdrawn, investigated context conditioning in molecular property prediction, highlighting the significant impact of fusion architecture and revealing potential flaws in standard benchmarking practices, such as data leakage. AI

IMPACT These studies highlight the potential and limitations of LLMs in specialized scientific domains, suggesting areas for future research in model calibration and robust evaluation.

RANK_REASON The cluster contains two academic papers discussing LLM applications in molecular property prediction and related benchmarking challenges.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

LLMs evaluated for molecular property prediction, showing mixed results on accuracy and uncertainty

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The cluster contains two academic papers discussing LLM applications in molecular property prediction and related benchmarking challenges.
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COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Matthias Busch, Marius Tacke, Tamadur Albaraghtheh, Sviatlana V. Lamaka, Mikhail L. Zheludkevich, Roland C. Aydin, Christian Feiler, Christian J. Cyron ·

    Do LLMs Know When They Are Wrong About Molecules? In-Context Property Prediction with Self-Reported Uncertainty

    arXiv:2610.41745v1 Announce Type: new Abstract: Large language models (LLMs) are used in many domains, but molecular property prediction with them is still little explored. In this work we evaluate the capability of 7 frontier LLMs to predict molecular properties together with a …

  2. arXiv cs.LG TIER_1 English(EN) · Bryan Cheng, Jasper Zhang ·

    When Does Context Help? A Systematic Study of Target-Conditional Molecular Property Prediction

    arXiv:2604.06558v2 Announce Type: replace Abstract: We present the first systematic study of when target context helps molecular property prediction, evaluating context conditioning across 10 diverse protein families, 4 fusion architectures, data regimes spanning 67-9,409 trainin…

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