Summary: Ribonucleic acids (RNAs) is a broad, yet underexploited, class of drug targets. We estimate that up to 70% of our genome encodes for RNAs, but only a tiny fraction of current pharmaceutical molecules is targeting them. Yet, mining this resource is a daunting task. Far beyond the capacity of classical physics-based computational simulation tools traditionally used to identify new drug candidates. Recent advances in machine learning technologies offer new opportunities to analyze this data, but they also require a vast amount of information to train them. In this project, we will use molecular docking software and massive experimental assays to build a comprehensive training set for our small molecule RNA binding predictor. The resulting software will be validated and exploited with our partner Takeda Pharmaceutical.
Lead Genome Centre: Génome Québec
User :
| Takahiko | Taniguchi | Takeda Pharmaceutical Company Limited |