Molecules, Vol. 29, Pages 1784: BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs

Molecules, Vol. 29, Pages 1784: BiMPADR: A Deep Learning Framework for Predicting Adverse Drug Reactions in New Drugs Molecules doi: 10.3390/molecules29081784 Authors: Shuang Li Liuchao Zhang Liuying Wang Jianxin Ji Jia He Xiaohan Zheng Lei Cao Kang Li Detecting the unintended adverse reactions of drugs (ADRs) is a crucial concern in pharmacological research. The experimental validation of drug–ADR associations often entails expensive and time-consuming investigations. Thus, a computational model to predict ADRs from known associations is essential for enhanced efficiency and cost-effectiveness. Here, we propose BiMPADR, a novel model that integrates drug gene expression into adverse reaction features using a message passing neural network on a bipartite graph of drugs and adverse reactions, leveraging publicly available data. By combining the computed adverse reaction features with the structural fingerprints of drugs, we predict the association between drugs and adverse reactions. Our models obtained high AUC (area under the receiver operating characteristic curve) values ranging from 0.861 to 0.907 in an external drug validation dataset under differential experiment conditions. The case study on multiple BET inhibitors also demonstrated the high accuracy of our predictions, and our model’s exploration of potential adverse reactions for HWD-870 has contributed to its research and development for market approval. In summary, ...
Source: Molecules - Category: Chemistry Authors: Tags: Article Source Type: research