Examining the Potential of ChatGPT on Biomedical Information Retrieval: Fact-Checking Drug-Disease Associations

Large language models (LLMs) such as ChatGPT have recently attracted significant attention due to their impressive performance on many real-world tasks. These models have also demonstrated the potential in facilitating various biomedical tasks. However, little is known of their potential in biomedical information retrieval, especially identifying drug-disease associations. This study aims to explore the potential of ChatGPT, a popular LLM, in discerning drug-disease associations. We collected 2694 true drug-disease associations and 5662 false drug-disease pairs. Our approach involved creating various prompts to instruct ChatGPT in identifying these associations. Under varying prompt designs, ChatGPT's capability to identify drug-disease associations with an accuracy of 74.6-83.5% and 96.2-97.6% for the true and false pairs, respectively. This study shows that ChatGPT has the potential in identifying drug-disease associations and may serve as a helpful tool in searching pharmacy-related information. However, the accuracy of its insights warrants comprehensive examination before its implementation in medical practice.

Predicting drug-disease associations by using similarity constrained matrix factorization

Background

Drug-disease associations provide important information for the drug discovery. Wet experiments that identify drug-disease associations are time-consuming and expensive. However, many drug-disease associations are still unobserved or unknown. The development of computational methods for predicting unobserved drug-disease associations is an important and urgent task.

Results

In this paper, we proposed a similarity constrained matrix factorization method for the drug-disease association prediction (SCMFDD), which makes use of known drug-disease associations, drug features and disease semantic information. SCMFDD projects the drug-disease association relationship into two low-rank spaces, which uncover latent features for drugs and diseases, and then introduces drug feature-based similarities and disease semantic similarity as constraints for drugs and diseases in low-rank spaces. Different from the classic matrix factorization technique, SCMFDD takes the biological context of the problem into account. In computational experiments, the proposed method can produce high-accuracy performances on benchmark datasets, and outperform existing state-of-the-art prediction methods when evaluated by five-fold cross validation and independent testing.

Conclusion

We developed a user-friendly web server by using known associations collected from the CTD database, available at http://www.bioinfotech.cn/SCMFDD/. The case studies show that the server can find out novel associations, which are not included in the CTD database.