Data-Driven Approach to Oligonucleotide Drug Discovery in the RNAHub

In this webinar, Rachapun Rotrattanadumrong, Machine Learning Scientist at Roche, presented the RNAHub, a multidisciplinary research community within Roche focused on advancing RNA therapeutics ― including oligonucleotides and siRNAs ― through a data-centric drug discovery approach. He outlined the in-house data pipeline used to integrate internal project data, which is typically enriched with publicly available patent data. These enriched datasets are then standardized and structured for machine learning (ML).

He also presented “OligoGym”, an open-source Python package developed by Roche to streamline the featurization, training, and evaluation of predictive models for oligonucleotide properties. Rachapun highlighted key challenges in applying ML to oligonucleotides, such as limited training data, and shared a case study on ML-guided gapmer design. 

This talk was given as part of our Open Forum event on data-driven advances in oligonucleotide therapy R&D, “The Data Advantage: Smarter Data Use. Smarter Oligos”. 

Who should watch? 

Biopharma and biotech professionals aiming to accelerate RNA therapy development through digital transformation, advanced data analytics, and AI/ML-driven strategies.