Oligonucleotide therapeutics offer great potential to address previously undruggable targets and enable personalized medicine. However, their progress is often hindered by insufficient safety and efficacy profiles. Predictive modeling and machine learning could significantly accelerate oligonucleotide drug discovery by identifying suboptimal compounds early on, but their application in this area lags behind other modalities. A key obstacle to the adoption of machine learning in the field is the scarcity of readily accessible and standardized datasets for model development, as data are often scattered across diverse experiments with inconsistent molecular representations. To overcome this challenge, we introduce OligoGym, a curated collection of standardized, machine learning-ready datasets encompassing various oligonucleotide therapeutic modalities and endpoints. We used OligoGym to benchmark diverse classical and deep learning methods, establishing performance baselines for each dataset across different featurization techniques, model configurations, and splitting strategies. Our work represents a crucial first step in creating a more unified framework for oligonucleotide therapeutic dataset generation and model training.

During the past 10 years, antisense-mediated exon skipping has proven to be a powerful tool for correction of mRNA splicing. For example, recently FDA-approved antisense oligonucleotides, including viltolarsen, eteplirsen, golodirsen, and milasen, were developed based on exon skipping technology. A significant challenge, however, is the difficulty in selecting an optimal target sequence for exon skipping. We have developed a computational method that takes into account many parameters as well as experimental data to design highly effective ASOs for exon skipping1, and improved this frame using a machine-learning algorithm. Shuntaro Chiba and Yasushi Okuno at the Molecular Design Data Intelligence Unit, RIKEN, Dr. Yoshitsugu Aoki at the Department of Molecular Therapy, National Center of Neurology and Psychiatry, and Dr.Toshifumi Yokota at the Department of Medical Genetics, University of Alberta, Faculty of Medicine and Dentistry. 1 Echigoya Y, Mouly V, Garcia L, Yokota T, Duddy W, In Silico Screening Based on Predictive Algorithms as a Design Tool for Exon Skipping Oligonucleotides in Duchenne Muscular Dystrophy.