Abstract
Identifying drug-disease associations is helpful for not only predicting new drug indications and recognizing lead compounds, but also preventing, diagnosing, treating diseases. Traditional experimental methods are time consuming, laborious and expensive. Therefore, it is urgent to develop computational method for predicting potential drug-disease associations on a large scale. Herein, a novel method was proposed to identify drug-disease associations based on the deep learning technique. Molecular structure and clinical symptom information were used to characterize drugs and diseases. Then, a novel two-dimensional matrix was constructed and mapped to a gray-scale image for representing drug-disease association. Finally, deep convolution neural network was introduced to build model for identifying potential drug-disease associations. The performance of current method was evaluated based on the training set and test set, and accuracies of 89.90 and 86.51% were obtained. Prediction ability for recognizing new drug indications, lead compounds and true drug-disease associations was also investigated and verified by performing various experiments. Additionally, 3,620,516 potential drug-disease associations were identified and some of them were further validated through docking modeling. It is anticipated that the proposed method may be a powerful large scale virtual screening tool for drug research and development. The source code of MATLAB is freely available on request from the authors.
Highlights
Traditional drug development usually follows this paradigm of one drug, one gene, one disease, which is an expensive and time-consuming process with stunningly high failure rate
The statistical results of AC, SE, SP, PR, and Matthew’s correlation coefficient (MCC), as well as receiver operating characteristic curve (ROC) and precision recall (RE) curve (PRC) derived from the training set and test set are shown in Figure 3 and listed in Table 2, respectively
The relative standard deviations are lower than 1%. These results reveal that the developed method can effectively capture information of drug-disease associations, and has a strong robustness for generating negative samples and an outstanding ability to identify drug-disease associations
Summary
Traditional drug development usually follows this paradigm of one drug, one gene, one disease, which is an expensive and time-consuming process with stunningly high failure rate. By conservative estimates, it takes about 15 years and $0.8–1.5 billion to bring a drug to market (Dudley et al, 2011; Yu et al, 2015), and during the development stage, almost 90% of the small molecules cannot pass the Phase I clinical trial and be eliminated (Wu et al, 2019). It is urgent to develop in silico drug redirecting approaches for discovering new indications for approved drugs on a large scale
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