Extended Data Fig. 6: Maximal common substructure identification reveals known antibiotic classes, but are less predictive than Chemprop rationales across all hits.

a,b, Rank-ordered numbers of hits (a) and non-hits (b) associated with maximal common substructures (MCSs) identified by a grouping method. Here, any hit associated with any of the MCSs shown shares a minimum of 12 atoms with the MCS. Dashed lines in MCSs indicate either single or double bonds. Each green or brown bar shows the prediction score of each MCS viewed as a molecule in its own right. Where bars are thin, the corresponding MCS prediction scores are approximately zero (including all brown bars in (b)). c,d, Similar to (a), but here, any hit associated with any of the MCSs shown shares a minimum of 10 (c) or 15 (d) atoms with the MCS. e, Illustration of the rationales (red) determined using a Monte Carlo tree search for example hits (black) associated with MCSs A1-A12. No hit associated with MCS A12 possessed a rationale. f, MCS prediction scores (blue bars) and the average prediction scores of all rationales of all hits associated with MCSs A1-A12 (red bars). Where blue bars are thin, the corresponding MCS prediction scores are approximately zero. No hit associated with MCS A12 possessed a rationale.