Masse, N. Y.; Lau, T. T. Y.; Wintersinger, J.; Slobodyanyuk, M.; Li, R.; Chu, V. S.; Zhu, H.; Kanuparthi, B.; La, J.; Fischbacher, B.; Arseneault, K.; Johnson, S.; Lu, W.-T.; Lorincz-Comi, N.; Bohn, E.; Cole, C. B.; Hoffman, G. R.; Lazar, N. H.; Findlay, S. D.; Celaj, A.; Frey, B. J.

Small interfering RNAs (siRNAs) silence target genes through RISC-mediated hybridization and cleavage, but can silence unintended transcripts and cause toxicity or complicate regulatory approval. In drug development, empirical assessment by qPCR and RNA-seq is slow, forces early candidate prioritization, and is limited by available species- and genome-appropriate cell models. While extensive empirical screening remains standard practice, there is a significant opportunity for modern AI methods to change this paradigm. We introduce DeepRNAi, a deep learning model that accurately predicts on- and off-target knockdown from siRNA and mRNA sequence. Trained on 16 million siRNA-transcript pairs from 2,031 siRNAs designed to interrogate known siRNA failure modes, and incorporating transcript context from biological foundation models, DeepRNAi is the first in silico method that approaches experimental accuracy. It generalizes across cell models, transcripts, genomes and species, and in a small study distinguishes approved therapeutics from hepatotoxic candidates. The value of DeepRNAi for prioritizing better candidates up front improving likelihood of success later in development is evidenced by a genome-wide survey of 24.4 million siRNAs targeting 7,946 genes, which indicates that 90.1% of the 100 most potent siRNAs have at least some off-target concerns. To support the community, we are publishing a training and benchmark dataset containing 1.84 million siRNA-transcript pairs from 240 siRNAs.