Fig. 4: Illustration of the generation and application of macromolecular graph representations for property prediction, cross-polymer comparison, and macromolecular interaction mechanism interpretation.
From: Sizing up feature descriptors for macromolecular machine learning with polymeric biomaterials
Transformation path of raw macromolecular structures in the workflow, first converted into SMILES text files, then network graphs. a Graph nodes correspond to monomers, edges correspond to bonds, both of which are attributed to vectorized molecular fingerprints describing aspects of their underlying molecules. b Exemplary pair-wise similarity matrix derived from dimension-reduced representations of macromolecular species across the training library. c GNN computation of various interaction prediction labels from input macromolecular graphs. d Post-hoc graph attribution analysis explains underlying structures important to model-assigned interaction predictions. Adapted with permission from ref. 15 (copyright Somesh Mohapatra, Joyce An and Rafael Gòmez-Bombarelli, 2022).
