The Role of Artificial Intelligence and Prompt Engineering in Forecasting Drug Tokisite

Artificial intelligence has the potential to revolutionize in the process of pharmaceutical development. Especially large language models (LLM) are increasingly interested in predicting the drug tokisite. But a new research reveals that these models are highly responsive to small changes. Making prompt engineering meticulously can directly affect the reliability of results.

The main findings of the research can be summarized as follows:

• Clinical experiments in the United Kingdom cost an average of 1,3 million pounds, resulting in about 90 percent of drugs with failure
• Toxicity is one of the most important causes of drug failure Home
• Traditional test methods are extremely dense in terms of time and cost
• LLMs can show unconsistent results even in minimal changes made in prompt

The pharmaceutical development industry needs fast and reliable forecasting tools. The high cost and low success rate of clinical experiments emphasizes the importance of alternative solutions. Although artificial intelligence has the potential to fill this cavity, especially in LLM, consistent issues are an important barrier.

This research shows that the standardization of prompt engineering process has critical importance. In areas with vital importance such as drug tokisity, artificial intelligence becomes reliable, requires careful and systematic approach. In the future, this technology will turn into clinical practice, depending on solving such basic problems.