Rate My Professor Xiaojun Yao

Professor Xiaojun Yao is a distinguished professor in the Faculty of Applied Sciences at Macao Polytechnic University. He earned a B.Sc. in Chemistry from Lanzhou University in 1998, a Ph.D. in Analytical Chemistry from Lanzhou University in 2003, and a Ph.D. in Chemoinformatics and Theoretical Chemistry from University Paris 7-Denis in 2004. Yao's academic career spans several prestigious institutions. From 2005 to 2013, he served as Professor in the College of Chemistry and Chemical Engineering at Lanzhou University. He then joined Macau University of Science and Technology as Professor at the State Key Laboratory for Quality Research in Chinese Medicines from 2013 to 2023. Since July 2023, he has held his current position at Macao Polytechnic University, where he contributes to advancements in the Center for Artificial Intelligence Driven Drug Discovery.

His research focuses on artificial intelligence in drug design and discovery, multi-scale molecular modeling of drug-target interactions, and molecular targeted anti-cancer drug discovery. Professor Yao has authored numerous influential publications in leading journals. Key works include 'An adaptive graph learning method for automated molecular interactions and properties predictions' (Nature Machine Intelligence, 2022), 'Upgrade of chrysomycin A as a novel topoisomerase II inhibitor to curb KRAS-mutant lung adenocarcinoma progression' (Pharmacological Research, 2022), 'Resistance looms for KRAS G12C inhibitors and rational tackling strategies' (Pharmacology & Therapeutics, 2022), 'RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictions' (Chemical Engineering Journal, 2021), 'TrimNet: learning molecular representation from triplet messages for biomedicine' (Briefings in Bioinformatics, 2021), and 'Discovery of a new inhibitor targeting PD-L1 for cancer immunotherapy' (Neoplasia, 2021). His contributions have garnered international recognition, including selection as one of the world's top 1% Highly Cited Researchers in Clarivate's 2025 list, underscoring his profound impact on computational drug discovery and pharmaceutical sciences.