ArticleJournal of chemical information and modeling2025
Augmented and Programmatically Optimized LLM Prompts Reduce Chemical Hallucinations.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Predicting Functional Diversity Within Enzyme Families: A Practical Perspective.Chembiochem : a European journal of chemical biology · 2026Review
- Computational data as the fuel for AI in chemistry.Chemical science · 2026Review
- AI-assisted development of interactive web simulations for analytical chemistry: a personal account.Analytical and bioanalytical chemistry · 2026Article
- A survey on large language models in biology and chemistry.Experimental & molecular medicine · 2026Review
- From Prompt to Drug: Toward Pharmaceutical Superintelligence.ACS central science · 2026Review
- Materials Dual-Source Knowledge Retrieval-Augmented Generation for Local Large Language Models in Photocatalysts.Journal of chemical information and modeling · 2025Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
Utilizing large Language models (LLMs) for handling scientific information comes with risk of the outputs not matching expectations, commonly called hallucinations. To fully utilize LLMs in research requires improving their accuracy, avoiding hallucinations, and extending their scope to research topics outside their direct training. There is also a benefit to getting the most accurate information from an LLM at the time of inference without having to create and train custom new models for each application. Here, augmented generation and machine learning-driven prompt optimization are combined to extract performance improvements over base LLM function on a common chemical research task. Specifically, an LLM was used to predict the topological polar surface area (TPSA) of molecules. By using augmented generation and machine learning-optimized prompts, the error in the prediction was reduced to 11.76 root-mean-squared error (RMSE) from 62.34 RMSE with direct calls to the same LLM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.