Ádám Arany
Research
Data integration and drug-target interaction prediction for the pharmaceutical industry
The research goal is to generate testable hypotheses based on an industrial scale dataset with millions of compounds and thousands of protein targets with 100s of millions of measured activity values. We should be able to derive reliable and well calibrated credibility intervals for our hypotheses to make the following decision making process possible. We develop and apply a Bayesian matrix factorization method Macau to make predictions in the incompletely filled drug-protein matrix, using different type of side-informations, e.g.: chemical structure fingerprints, protein target similarities, imaging-based High-Content Screening (HCS) results.
Active projects
Finished projects
- CELSA - Active Learning - Active learning and interpretation of computational model on tissue specific permeability for stock and virtual drug discovery library
- ChemBioBridge - Bridging chemical and biological experimental design for pharma industry through data fusion.
- Melloddy - MachinE Learning Ledger Orchestration for Drug DiscoverY
Publications
301 Moved Permanently
Contact information
- Office: 05.11
- Address:
ESAT
Kasteelpark Arenberg 10, bus 2446, B-3001 Leuven, Belgium - Tel: +32 16 32 44 91
- Email: adam.arany@esat.kuleuven.be