Our scientific Heritage

Our science is based on an extensive academic and industry track record. We have published articles in top journals in the field

Protein structure–based gene expression signatures
Peer-reviewed papers
Improving the efficacy-safety balance of polypharmacology in multi-target drug discovery
Peer-reviewed papers
Learning with multiple pairwise kernels for drug bioactivity prediction
Peer-reviewed papers
Transcriptomic profiling of human cardiac cells predicts protein kinase inhibitor-associated cardiotoxicity
Peer-reviewed papers
Our team previously launched an international DREAM challenge to predict kinase-ligand interactions
Crowdsourced mapping of unexplored target space of kinase inhibitors
Peer-reviewed papers
Our team previously developed machine learning models for predicting pharmacologically relevant kinase conformational states
Redefining the Protein Kinase Conformational Space with Machine Learning
Peer-reviewed papers
Our team previously developed methods to analyze the chemogenomic landscape of kinases and their inhibitors
Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies
Peer-reviewed papers