Finding new drugs with generative models
One field that we have not covered in this volume in which generative AI is making a large impact is biotechnology research. We discuss two areas: drug discovery and predicting the structure of proteins.
Searching chemical space with generative molecular graph networks
At its base, a medicine – be it drugstore aspirin or an antibiotic prescribed by a doctor – is a chemical graph consisting of nodes (atoms) and edges (bonds) (Figure 13.2). Like the generative models used for textual data (Chapters 3, 9, and 10), graphs have the special property of not being fixed length. There are many ways to encode a graph, including a binary representation based on numeric codes for the individual fragments (Figure 13.2) and "SMILES" strings that are linearized representations of 3D molecules (Figure 13.3). You can probably appreciate that the number of potential features in a chemical graph is quite large; in fact, the number...
