Predicting drug drug interactions for enhancing patient safety
Predicting base editing efficiencies in both lab cells and living models
Predicting drug synergy for enhancing therapeutic effectiveness
Training AI algorithms on metagenomic data for predicting CAS9 PAM motifs
Predicting prime editing efficiencies and optimizing prime editing guide RNA (pegRNA) design
TnpB editing efficiency predictor
Generative AI for protein fitness optimization
We are proud to be part of the UZH University Research Priority Programs (URPP) where we together with the Schwank lab investigate the use of AI 🐱💻 and CRISPR-based technologies ✂️ 🧬 for germline editing. Our efforts aim at providing policy guidance on the societal impact and legal challenges of rapidly changing medical technology on human reproduction. The goals and objectives of the subproject SP4 CRISPR Technology in Human Reproduction are detailed below (taken from the URPP Human Reproduction Reloaded official website ).
Novel computational method for drug-drug interaction predictions which are an important consideration for patient treatment.