# DeepRNAi

Deep Genomics subtracts the whole-transcriptome perturbation response that virtual-cell models usually try to predict, treating it as noise, and trains a sequence model, DeepRNAi, only on the direct effects between an siRNA and its target transcript. Direct effects account for about 1% of all siRNA-RNA interactions, and uncalibrated virtual-cell objectives had treated that signal as a rounding error. On a held-out-transcript split, DeepRNAi performs about the same as on seen transcripts, while a conventional machine-learning approach without a direct-effect focus loses roughly 50% of its above-baseline performance.

Deep Genomics subtracts the whole-transcriptome perturbation response that virtual-cell models usually try to predict, treating it as noise, and trains a sequence model, DeepRNAi, only on the direct effects between an siRNA and its target transcript. Direct effects account for about 1% of all siRNA-RNA interactions, and uncalibrated virtual-cell objectives had treated that signal as a rounding error. On a held-out-transcript split, DeepRNAi performs about the same as on seen transcripts, while a conventional machine-learning approach without a direct-effect focus loses roughly 50% of its above-baseline performance.

Deep Genomics' DeepRNAi combines the local siRNA hybridization site with long-range context from the genomic region around the transcript, supplied by biological foundation models BigRNA, Enigma and REPRESS over up to one million base pairs of sequence. On seed-mediated knockdown prediction, DeepRNAi without the foundation models performs in aggregate about as well as a random forest with hand-built transcript features; with the foundation models, performance nearly doubles. In two drug-discovery cases the model predicted near-zero activity for siRNAs targeting the distal 3' UTR, matching Alnylam's published PLG experimental data.

Deep Genomics' DeepRNAi combines the local siRNA hybridization site with long-range context from the genomic region around the transcript, supplied by biological foundation models BigRNA, Enigma and REPRESS over up to one million base pairs of sequence. On seed-mediated knockdown prediction, DeepRNAi without the foundation models performs in aggregate about as well as a random forest with hand-built transcript features; with the foundation models, performance nearly doubles. In two drug-discovery cases the model predicted near-zero activity for siRNAs targeting the distal 3' UTR, matching Alnylam's published PLG experimental data.