T. Vinh, L. Nguyen, Q. H. Trinh, T-H Nguyen-Vo, and B. P. Nguyen∗
In drug discovery, the search for new and effective medications is often hindered by concerns about toxicity. Numerous promising molecules fail to pass the later phases of drug development due to strict toxicity assessment. This challenge significantly raises the costs, time, and human effort needed to discover new therapeutic molecules. Additionally, a considerable number of drugs already on the market have been withdrawn or reevaluated because of their unwanted side effects. Among the various types of toxicity, drug-induced heart damage is a severe adverse effect commonly associated with several medications, especially those used in cancer treatments. Although several computational approaches were proposed to identify the cardiotoxicity of molecules, existing approaches show limited performance and interpretability. In our study, we proposed a more effective computational framework to predict the cardiotoxicity of molecules using an Attention-based Graph Neural Network.
Experimental results indicated that our method outperformed the other methods. Also, an assessment of model stability confirmed the model's applicability. To assist researchers in evaluating the cardiotoxicity of molecules, we have developed an easy-to-use online web server that incorporates our model.
The code and data are now available upon request. They will then be available for download after this study is published.