A/Professor Jiangning Song


  • Infection and Immunity; Bioinformatics; Artificial Intelligence
  • New drug design using machine learning
  • Artificial intelligence
  • Modifying last-line antibiotics against Pathogens

Jiangning's main research interests are bioinformatics, heterogeneous data modelling, machine learning, and data analytics in the fields of infection and immunity, and cancer biology. Trained as a bioinformatician and data-savvy scientist, he has a very strong specialty in Artificial Intelligence, Bioinformatics, Comparative Genomics, Cancer Genomics, Bacterial Genomics, Computational Biomedicine, Data Mining, Infection and Immunity, Machine Learning, Proteomics, and Biomedical Big Data Analytics, which are highly sought-after expertise and skill sets in the data-driven, paradigm-shifting biomedical research.

Jiangning Song is an Associate Professor and Group Leader in the Cancer and Infection and Immunity Programs at Monash Biomedicine Discovery Institute (BDI), and Department of Biochemistry and Molecular Biology Monash University, Australia. He is head of the AI-Driven Bioinformatics and Computational Biomedicine Laboratory in the Monash BDI and an Associate Investigator of the ARC Centre of Excellence in Advanced Molecular Imaging at Monash University. He is also a member of the Monash Centre for Data Science, Faculty of Information Technology; the Monash Data Futures Insitute (MDFI); the Monash Alliance for Digital Health (ADAM); and the Monash Bioinformatics Platform.


  • Integrating AI, Big Data and Smart Drug Design against ‘Superbugs’
  • Integrative systems pharmacology, neutron reflectometry and molecular dynamics to examine interactions of polymyxins and bacterial membranes.
  • Integrated virtual cells: elucidating the systems pharmacology of antibiotics against Pseudomonas aeruginosa & paradigm-shifting in antibiotic discovery.
  • Targeting outer membrane vesicles of Acinetobacter baumannii: A systems and computational approach.
  • New tricks for ‘old’ drugs: PK/PD of polymyxin nonantibiotic combinations.
  • Machine learning methods for identifying human-pathogen protein-protein interactions.


  • Predicting protein sites for drugs or repurposing drugs.
  • New drug design using machine learning and artificial intelligence, for example in phage characterisation and deployment.