An AI pipeline that designs ranked, safety-checked dsRNA candidates — in minutes, not months. Powered by local Llama 3.2 3B, PyTorch, and a 14-species safety panel.
Click a pest card to design dsRNA candidates. The pipeline runs immediately — no waiting for LLM parsing.
Click a pest target card (7 species supported) or describe the problem in natural language.
PyTorch backend tiles pest transcripts into 200-nt dsRNA precursors, then dices them into 21-nt siRNAs.
Dilated CNN scores each siRNA for efficacy. K-mer index checks off-target risk against 14 species.
Physics-Informed Neural Network predicts environmental half-life based on sequence and field conditions.
Learned ranker combines efficacy, safety, and fate into a final score. Top candidates are selected.
Virtual wet-lab runs 1000 Monte Carlo trials per candidate, modeling delivery, uptake, Dicer, RISC, and knockdown.