Xutong Li

TL;DR: A new graph neural network architecture called Attentive FP for molecular representation that uses a graph attention mechanism to learn from relevant drug discovery datasets and achieves state-of-the-art predictive performances on a variety of datasets and that what it learns is interpretable.

TL;DR: Recently, due to the strong generalization ability and powerful feature extraction capability, deep learning methods have been employed in predicting the molecular properties as well as generating the desired molecules, which will further promote the application of AI technologies in the field of drug design.

TL;DR: A virtual profiling model against a panel of 391 kinases based on large-scale bioactivity data and the multitask deep neural network algorithm is presented to create a comprehensive kinome interaction network for designing novel chemical modulators or drug repositioning.

TL;DR: In this article, a modified intergranular fatigue cracking mechanism was suggested, and the significant role of the grain boundary triple lines and triple junctions (TJs) in fatigue damage was revealed.

TL;DR: In this paper, the authors summarize the applications of generative models to drug design, including generating various compounds to expand the compound library and designing compounds with specific properties, and also list a few publicly available molecular design tools based on Generative models which can be used directly to generate molecules.