When AlphaFold, a deep neural network that predicts the structures of proteins from their amino acid sequences, won the 2020 CASP competition, deep neural networks debuted as full members of scientific society. With its potential to accelerate drug development and medical research, AlphaFold represents AI at its most virtuous, and it has become the preeminent example of deep learning in science. At the same time, AlphaFold is a stark demonstration that the scientific understanding of a process is often completely irrelevant to a deep neural network’s ability to make predictions about that process. Just as AlphaFold’s tectonic impact on the fields of molecular and cellular biology indicates how valuable a fast and reliable predictive system can be, the specifics of its design make clear that understanding may now be superfluous for such systems. AlphaFold’s creation may very well mark the first time that scientists have managed to thoroughly dissociate prediction and understanding.

This chapter explores the molecular foundations of heredity and the discovery that life is governed by a discrete, information-bearing code. Beginning with Mendel’s experiments on inheritance, it traces the path from abstract genetic ratios to the identification of DNA as the physical carrier of genetic information. The chapter explains how DNA’s four-letter alphabet encodes biological structure, how its double-helix structure enables accurate replication, and how information flows from DNA to RNA to proteins through transcription and translation. The discrete nature of the genetic code allows for stability, error correction, and long-term evolution, drawing deep parallels with digital information systems. The chapter then examines protein folding as both a biological and computational problem, highlighting why predicting protein structure is difficult in theory yet reliably solved by nature. Recent breakthroughs using machine learning, particularly AlphaFold, illustrate how computational approaches can uncover biological structure without explicit physical simulation. Finally, the chapter turns to gene editing technologies such as CRISPR, showing how biology has become programmable and raising profound questions about the future of medicine, evolution, and human intervention in life’s code.