Shuangjia Zheng

TL;DR: Wang et al. as mentioned in this paper developed a deep learning-based CT diagnosis system to identify patients with COVID-19, which achieved an AUC of 0.99, recall (sensitivity) of 0.,93, and precision of 0,96.

TL;DR: A deep learning-based CT diagnosis system (DeepPneumonia) was developed and showed that the established models can achieve a rapid and accurate identification of COVID-19 in human samples, thereby allowing identification of patients.

TL;DR: This study has developed a template-free self-corrected retrosynthesis predictor (SCROP) to predict retroSynthesis by using Transformer neural networks, which was 1.7 times more accurate than other state-of-the-art methods for compounds not appearing in the training set.

TL;DR: An end-to-end deep learning framework to predict the interactions of proteins with potential drugs by representing proteins with a two-dimensional distance map from monomer structures and drugs with molecular linear notation, following the visual question answering mode is proposed.

TL;DR: In this paper , the authors report the combined use of unsupervised machine learning (involving vector representations of molecular structures, pharmacophore fingerprinting and conformer fingerprinting) and a cross-species approach for the screening and experimental validation of new mitophagy-inducing compounds.