Empowering AI in RNAi Therapeutics: A Foundational Dataset for siRNA Design and Optimization

The rational engineering of siRNAs poses a principal obstacle in the progression of RNAi therapeutics, encompassing hurdles in delivery, stability, and mitigation of unintended effects. While early public repositories contained limited siRNA entries, contemporary innovations have substantially broadened siRNA utilization, culminating in clinical substantiation and extensive deployment of novel agents. For instance, landmark approvals of drugs like Amvuttra (Patisiran) and Leqvio (Inclisiran) have dramatically augmented the therapeutic landscape for siRNAs and affirmed their clinical efficacy and market feasibility. Chemical modifications are critical for improving pharmacokinetics and reducing immune activation, the rules governing their effects are poorly understood. The current data landscape—composed of limited, publicly accessible datasets—prevents researchers from answering key scientific questions about how sequence, modification, and experimental context jointly determine silencing efficacy. Without a sufficiently rich and diverse foundational dataset, AI-guided siRNA design cannot achieve its full potential.