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Drug vector representation and potential efficacy prediction based on graph representation learning and transcriptome
Jian-Ping Deng1, Xin Liu1, Yue Li1
1The First Affiliated Hospital, Guangzhou University of Chinese Medicine, Guangzhou, 510407, China; Lingnan Medical Research Center, Guangzhou University of Chinese Medicine, Guangzhou, 510407, China; University Key Laboratory of Traditional Chinese Medicine Prevention and Treatment of Chronic Heart Failure, Guangdong Province, Guangzhou, 510407, China.
Ethnopharmacological Relevance:
Acacetin is widely distributed in traditional Chinese medicine and traditional herbs, with strong biological activity. Perhaps there are many potential effects that have not been explored. In the field of drug discovery, Mainstream methods focus on chemical structure. Traditional medicine cannot adapt to the mainstream prediction methods due to its complex composition.
Aim Of The Study:
Our aim is that provide a prediction method more suitable for traditional medicine by graph representation learning and transcriptome data. And use this method to predict acacetin.
Materials And Methods:
Our method mainly consists of two parts. The first part is to use the method of graph representation learning to vectorize drugs as a database. The original data of this part comes from transcriptome data on Gene Expression Omnibus. The method of graph representation learning is an unsupervised learning. If there is no prior knowledge as the label data, the training effect cannot be analyzed. Therefore, we define a standard score to evaluate our results through the idea of Jaccard index. The second part is to put the target drug into our database. The potential similarity between drugs was evaluated by the Euclidean distance between vectors, and the potential efficacy of the target drug is predicted by combining the chemical-disease relationship data in the Comparative Toxicogenomics Database. The target drug in this paper uses acacetin. We compared the predicted results with existing reports, and we also experimentally verified the efficacy of improving insulin resistance in the predicted results.
Results:
The prediction results are relatively consistent with the existing reports, which demonstrated that our method has a certain degree of predictive performance. And for the efficacy of improving insulin resistance in the predicted result, we verified it through experiments.
Conclusions:
We propose a method to predict the potential efficacy of drugs based on transcriptome data, using Graph representation learning, which is very suitable for traditional medicine. Through this method, we predicted the efficacy of acacetin, and the results are relatively consistent with the current reports. This provides a new idea for unsupervised learning to apply medical information.
