Virtual Kinome Profiler is a compound-centric computational platform that facilitates in-silico profiling of compounds across the druggable kinome. The platform prioritizes potent compound-kinase interactions by enumerating the compound-set similarity and dissimilarity estimates across each molecular kinase and further utilizes an ensemble machine learning classifier in evaluating potent interactions. The platform currently encloses 248 druggable kinases spanning across different kinome subfamilies and merely requires the structural description of the compounds (SMILES) for the predictions task (see example). The output from the analysis can be downloaded as a .csv or .pdf file for further pre-processing. Virtual Kinome Profiler is designed to expedite and augment the kinome-specific drug discovery process significantly overcoming the intrinsic limitations of time and cost accompanying traditional drug discovery protocol.


Chemogenomic Analysis of the Druggable Kinome and Its Application to Repositioning and Lead Identification Studies. Balaguru Ravikumar, Sanna Timonen, Zaid Alam, Elina Parri, Krister Wennerberg, Tero Aittokallio;
Cell Chemical Biology 2019 doi:https://doi.org/10.1016/j.chembiol.2019.08.007


For information, comments and/or suggestions, please contact us at:

FIMM - Aittokallio group
Computational Systems Medicine
FI-00014 University of Helsinki
Tukholmankatu 8,Biomedicum Helsinki 2U

email: balaguru.ravikumar@gmail.com