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LLM prompt engineering shows limited impact on drug toxicity prediction, study finds

By PulseAugur Editorial · [1 sources] ·

A new research paper explores the effectiveness of prompt engineering for predicting drug toxicity using large language models (LLMs). The study found that the inherent variance in LLMs significantly outweighs the impact of prompt optimization, suggesting that prompt phrasing has limited influence on prediction accuracy. However, the research did demonstrate substantial performance improvements when using chemoinformatic code for feature extraction compared to LLM-generated values. The proposed methodology is applicable to various prompt types in bioinformatics. AI

IMPACT Suggests that while LLMs are useful for drug discovery, their inherent variability requires careful consideration of feature extraction methods over prompt optimization.

RANK_REASON Research paper published on arXiv detailing a new methodology for analyzing prompt engineering in LLMs for drug toxicity prediction. [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 prompt engineering shows limited impact on drug toxicity prediction, study finds

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Research paper published on arXiv detailing a new methodology for analyzing prompt engineering in LLMs for drug toxicity prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Mia MacGregor, Aakash Welgamage Don, Mark Bartlett ·

    Analysis of Prompt Engineering for Drug Toxicity Prediction

    arXiv:2609.03635v1 Announce Type: new Abstract: Clinical trials in the UK can cost up to {\pounds}1.3 million, with approximately 90% drug failure rate. Toxicity is a major contributing factor in drug failure. Testing is time and cost intensive. In recent years, the use of artifi…

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