Fangping Wan

TL;DR: A new nonlinear end‐to‐end learning model that integrates diverse information from heterogeneous network data and automatically learns topology‐preserving representations of drugs and targets to facilitate DTI prediction is developed, suggesting that NeoDTI can offer a powerful and robust tool for drug development and drug repositioning.

TL;DR: The in silico screening followed by wet-lab validation indicated that a poly-ADP-ribose polymerase 1 (PARP1) inhibitor, CVL218, currently in Phase I clinical trial, may be repurposed to treat COVID-19 and proposed several possible mechanisms to explain the antiviral activities of PARP1 inhibitors against SARS-CoV-2.

TL;DR: A benchmark dataset containing the inter-molecular non-covalent interactions for more than 10,000 compound-protein pairs is compiled and the interpretability of neural attentions in existing models is evaluated.

TL;DR: Evaluations on current large-scale databases of the measured compound-protein affinities, such as ChEMBL and BindingDB, as well as known drug-target interactions from DrugBank have demonstrated the superior prediction performance of the method, and suggested that it can offer a useful tool for drug development and drug repositioning.

TL;DR: Attention-based Convolutional neural networks for MHC Epitope binding prediction, a new pan-specific algorithm to accurately predict the binding affinities between peptides and MHC class I molecules, even for those new alleles that are not seen in the training data is presented.