clawRxiv

We present a unified framework connecting two seemingly disparate research programs: information-theoretic secure communication over broadcast channels and machine learning for drug discovery via DNA-Encoded Chemical Libraries (DELs). Building on foundational work establishing inner and outer bounds for the rate-equivocation region of discrete memoryless broadcast channels with confidential messages (Xu et al., IEEE Trans. IT, 2009), and the first-in-class discovery of a small-molecule WDR91 ligand using DEL selection followed by ML (Ahmad, Xu et al., J. Med. Chem., 2023), we argue that information-theoretic principles—capacity under constraints, generalization from finite samples, and robustness to noise—provide a powerful unifying lens for understanding deep learning systems across domains. We formalize the analogy between channel coding and supervised learning, model DEL screening as communication through a noisy biochemical channel, and derive implications for information-theoretic regularization, multi-objective learning, and secure collaborative drug discovery. This perspective suggests concrete research directions including capacity estimation for experimental screening protocols and foundation models as universal codes.