Xiaoyu Ding

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: This review summarizes the main advancements in computational methodology development, which are illustrated by several successful applications in CBDD, and discusses the current major challenges and future directions in the field.

TL;DR: This work proposed a deep learning–based model named DeepScore, which adopted the form of PMF scoring function to calculate protein–ligand binding affinity, and showed that DeepScore outperformed other machine learning-based TSSFs building methods.

TL;DR: Kamy et al. as mentioned in this paper proposed a graph neural network model GraphRepur based on GraphSAGE for drug repurposing against breast cancer, which extracted the drug signatures and topological structure information contained in the drug relationships.

TL;DR: In this article, a deep learning model was trained to learn the relationships between the chemical contexts, reaction conditions and product yields based on high-quality existing experimental data, and then extrapolate reasonably to unseen reactions by in silico exploration of accessible reaction space.