Recoding Genomic Elements with AI and Quantum Computation to Build the Next Generation Drug Discovery Platform

The traditional view of the genome has largely centered on protein-coding and non-coding regions, as these parts show clear observable functions. The non-coding sequences previously considered “junk DNA” have been extensively studied for the last few decades and are recognized to encode regulatory elements. This shift is expanding our understanding of genome complexity and its hidden potential. The non expressing sequences have not received much attention. To our best knowledge, we demonstrated for the first time that naturally non-expressing intergenic DNA sequences from Escherichia coli and Saccharomyces cerevisiae can be synthetically expressed to produce functional molecules. Leveraging this insight and recognizing the limited innovation in current drug discovery efforts, often relying on derivatives of existing or repurposed drugs — we propose a next-generation, first-in-class drug discovery platform that harnesses the vast, untapped genomic landscape—comprising non-expressed DNA sequences, non-translating sequences, and retired gene elements—through the integration of Artificial Intelligence (AI) and Quantum Computing (QC). The AI would enable high-throughput prediction of functional molecules from this untapped genomic reservoir, while QC would provide unprecedented molecular simulation capabilities to identify optimal molecules for specific targets. We are on the cusp of a transformative opportunity to recode naturally occurring DNA sequences from model organisms into a novel therapeutic pipeline and integrate Artificial Intelligence, Quantum Computing, and deep genome science, for the first time, to drive next-generation drug discovery. This integrative approach of reprogramming latent genomic code paves the way for building a deep genome foundry leading to first-in-the-class drug discovery pipeline.