From the discovery of new drug candidates through clinical trials to their approval, it takes approximately 15 years to launch a new drug into the market, and costs approximately one trillion to two trillion won. Despite several improvements in the drug development pipeline over the past 30 years, failures have skyrocketed at all stages of clinical trials owing to safety reasons. To improve the success rate of clinical trials, it is necessary to identify drug candidates that may fail in the clinical trials. Therefore, we need to develop reliable models to predict the outcomes of clinical trials of drug candidates. In this paper, we propose a deep multimodal classification model based on informative chemical features of the drugs and targetbased features. Experimental results reported on the PrOCTOR dataset indicate that the proposed model performs better in a multimodal setting. Comparing ensemble models based on random forests and extra trees, the proposed deep multimodal classifier obtains the highest value for the area under the receiver operator curve and area under the precision-recall curve. The results of this study demonstrate that the proposed multimodal classifier can be used to predict the outcomes of clinical trials effectively.