Area of research
Computational Theory and Mathematics · Materials Chemistry
Research interest
Research focused on Fragment (logic) and Artificial intelligence, with related work in Deep learning, Drug discovery, Bromodomain. Notable publications include 'SyntaLinker: automatic fragment linking with deep conditional transformer neural networks', 'Accelerated rational PROTAC design via deep learning and molecular simulations', and 'Structure-Aware Multimodal Deep Learning for Drug–Protein Interaction Prediction'.
NAT10-mediated mRNA N4-acetylcytidine reprograms serine metabolism to drive leukaemogenesis and stemness in acute myeloid leukaemia
Discovery of orally bioavailable SARS-CoV-2 papain-like protease inhibitor as a potential treatment for COVID-19
The recent progress of deep-learning-based in silico prediction of drug combination
Accelerated rational PROTAC design via deep learning and molecular simulations
Structure-Aware Multimodal Deep Learning for Drug–Protein Interaction Prediction
DRlinker: Deep Reinforcement Learning for Optimization in Fragment Linking Design
Deep scaffold hopping with multimodal transformer neural networks
SyntaLinker: automatic fragment linking with deep conditional transformer neural networks
A systematic benchmarking of <sup>31</sup>P and <sup>19</sup>F <scp>NMR</scp> chemical shift predictions using different <scp>DFT</scp>/<scp>GIAO</scp> methods and applying linear regression to improve the prediction accuracy