Decoding Life’s Blueprint with AI

The model architecture mirrors the central dogma of molecular biology-capturing genomic relationships via DNA self-attention within a [latex] \pm 57 \text{ kb} [/latex] window, gene co-regulation through RNA self-attention, and transcriptional control via cross-attention-and integrates these modalities with a Virtual Cell Embedder to predict perturbation effects, a design precisely replicated in CDT-III’s VCE-N, allowing for complete weight transfer and a continuation of the established predictive framework.

A new artificial intelligence model aligns with the fundamental principles of molecular biology to predict how cells respond to change and assess potential drug safety.