Prediction of Drug-Likeness Using Deep Autoencoder Neural Networks - Hu, Qiwan; Feng, Mudong; Lai, Luhua; Pei, Jianfeng (Frontiers in Genetics, 2018)
Due to diverse reasons, most drug candidates can not eventually become marketed drugs. Developing reliable computational methods for prediction of druglikeness of candidate compounds is of vital importance to improve the success rate of drug discovery and development. In this study, we used deep autoencoder neural networks to construct druglikeness classification models. We collected datasets of drugs (represented by ZINC World Drug), bioactive molecules (represented by MDDR and WDI), and common molecules (represented by ZINC All Purchasable and ACD). Compounds were encoded with MOLD2 two-dimensional structure descriptors. The classification accuracies of druglike/non-druglike model are 91.04% on WDI/ACD databases, and 91.20% on MDDR/ZINC, respectively. The performance of the models outperforms previously reported models. In addition, we develop a drug/non-druglike model (ZINC World Drug vs ZINC All Purchasable), which distinguishes drugs and common compounds, with a classification accuracy of 96.99%. Our work shows that by using high-latitude molecular descriptors, we can apply deep learning technology to establish state-of-the-art druglikeness prediction models.