AGPred: An End-to-End Deep Learning Model to Predicting Drug Approvals in Clinical Trials Based on Molecular Features.

One of the major challenges in drug development is maintaining acceptable levels of efficacy and safety throughout the various stages of clinical trials and successfully bringing the drug to market. However, clinical trials are time-consuming and expensive. While there are computational methods designed to predict the likelihood of a drug passing clinical trials and reaching the market, these methods heavily rely on manual feature engineering and cannot automatically learn drug molecular representations, resulting in relatively low model performance. In this study, we propose AGPred, an attention-based deep Graph Neural Network (GNN) designed to predict drug approval rates in clinical trials accurately. Unlike the few existing studies on drug approval prediction, which only use predicted targets of compounds, our novel approach employs a GNN module to extract high-potential features of compounds based on their molecular graphs. Additionally, a cross-attention-based fusion module is utilized to learn molecular fingerprint features, enhancing the model's representation of chemical structures. Meanwhile, AGPred integrates the physicochemical properties of drugs to provide a comprehensive description of the molecules. Experimental results indicate that AGPred outperforms four state-of-the-art models on both benchmark and independent datasets. The study also includes several ablation experiments and visual analyses to demonstrate the effectiveness of our method in predicting drug approval during clinical trials. The codes for AGPred are available at https://github.com/zhc940702/AGPred.