Our mission is to develop mathematical, statistical and informatics tools to tackle biomedical questions that may potentially lead to breakthroughs in drug discovery. We are focusing on network pharmacology modeling, aiming at a systems-level understanding of how cancer signaling pathways can be inhibited by synergistic drug combinations through multi–target perturbations. These methods offer an improved efficiency to identify more effective cancer treatments for personalized medicine.
@NetPharMed

How to predict drug combinations in the most difficult scenarios where no training data is available? Transfer lear… twitter.com/i/web/status/1…

Data analysis revealed new biology of anti-CD73 therapies: onlinelibrary.wiley.com/doi/10.1002/ej…

Enlightening cancer drug discovery with challenge-winning AI solutions | University of Helsinki… twitter.com/i/web/status/1…

Very happy that our work on the modeling of the concepts in traditional medicine with multipartite networks is out… twitter.com/i/web/status/1…