An interdisciplinary approach in teaching RNA secondary structure prediction to first-year undergraduate students using an automated deep learning RNA 3D model prediction tool.

The use of artificial intelligence (AI) in biologics drug design is interlaced into the fabric of the drug discovery pipeline for many in the biotechnology industry. The use of AI tools in RNA therapeutic drug design has gained traction in recent years to develop more effective therapeutics in a short period of time, revolutionizing rapid-response therapeutics. Indeed, machine learning (ML) and deep learning (DL) are streamlining RNA therapeutic design in ways we never thought were possible just a decade ago. These advances are accompanied by a plethora of new AI tools for drug design that continue to barrage the research space at unprecedented speed. As biology educators, we bear the responsibility for keeping up with technological advances in the biotechnology space, as it is up to us to prepare and equip the next generation of scientists with the use of AI platforms in this space. Small interfering RNA (siRNA) therapeutic design remains a complex challenge, despite several of them being currently in clinical use for various genetic diseases. The application of AI and ML models can predict potent and longer-lasting siRNA drug candidates for therapeutic development. Additionally, it is imperative that siRNA candidates are screened for their propensity to form secondary structures, as this can reduce targeting efficacy and result in unwanted immune responses. Though siRNA technology is commonly taught at the undergraduate level across life sciences disciplines, there remains a disconnect between the use of AI and siRNA design in the teaching curriculum. We previously described an innovative approach for teaching students the use of a generative AI tool called Biomod AI to design siRNAs. Here, we designed an inquiry-based non-wet-lab workshop for students to explore the use of an automated DL-based RNA 3D structure prediction tool called trRosettaRNA to determine the secondary structures of siRNAs. Importantly, the interdisciplinary design of this activity amalgamates both AI and RNA science concepts in a simplified format in a single workshop tailored for first-year health sciences undergraduate students.