The rapid evolution of in silico tools is revolutionizing RNAi therapeutic development, from target discovery to regulatory approval. Attendees will learn how to leverage these tools to expand RNAi applications beyond

current frontiers, optimize drug stability, and navigate regulatory hurdles. A must for teams aiming to harness data-driven approaches for faster, smarter RNAi therapeutic design

• Deploying AI-powered genomic and transcriptomic analysis to uncover high-confidence RNAi targets, and directed evolution-based discovery of novel protein-to-protein interactions governing tissue-selective, extrahepatic delivery of siRNA.
• Applying machine learning to predict off-target effects and potency, streamlining lead candidate selection, and reducing late-stage attrition in preclinical development
• Utilizing computational modelling to connect sequence and structure with functional data layers, such as predicting ligands for orphan receptors, binding affinity, internalization dynamics and stability, to accelerate discovery and translation of early leads.