Analysis of Prompt Engineering for Drug Toxicity Prediction
Quick summary
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 artificial intelligence has been increasingly explored to aid in the prediction of drug toxicity, with extensive use of large language models (LLMs). However, LLMs can show considerable variation when minor changes are made to prompts, which raises concerns about their sensitivity to prompt engineering. Prompt engineering is
Key takeaways
- 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.
Why it matters
“Analysis of Prompt Engineering for Drug Toxicity Prediction” illustrates how changes in the AI ecosystem can affect products, workflows and user expectations together. Its lasting significance depends on measurable adoption, cost and safety outcomes.
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