Symbolic Fragment Weighting for Substructural Analysis: Genetic Programming vs Genetic Algorithm on ChEMBL
Mohd Isrul Esa, Nor Samsiah Sani, Salwani AbdullahIntroduction:
Substructural Analysis is a scalable and chemically interpretable approach to ligand-based virtual screening, but ranking quality depends strongly on the fragmentweighting model used. This study benchmarks Genetic Programming for evolving symbolic fragment-weighting equations against a Genetic Algorithm baseline that directly optimises integer fragment weights under a fixed Substructural Analysis scoring structure.
Methods:
Using a ChEMBL-derived benchmark of 15 activity classes represented by MACCS 166-bit fingerprints, we conducted 10 runs per class under a stratified 90/10 train–test protocol. Early-recognition performance was evaluated using enrichment factor at the top 1% (EF@1%) on the test partition. Between-method differences were tested using a two-sided Mann–Whitney U test (α = 0.05), with Benjamini–Hochberg adjustment across activity classes.
Results:
The Genetic Algorithm baseline achieved higher median EF@1% than the Genetic Programming approach in most activity classes, with statistically significant advantages in 11 of 15 classes before adjustment and 10 of 15 classes after Benjamini–Hochberg adjustment. The Genetic Programming approach remained competitive for COX, 5HT1A, 5HT3, and ACHE; for COX, it produced a higher median EF@1% than the Genetic Algorithm baseline (19.05 vs 16.67), although the difference was not statistically significant (p = 0.333).
Discussion:
These findings suggest that symbolic fragment-weighting is feasible and inspectable, but that enrichment performance is sensitive to how the optimisation objective is defined for early-recognition screening.
Conclusion:
Genetic Programming can evolve symbolic Substructural Analysis weighting equations; however, the observed performance gap suggests that future GP fitness designs should be more directly aligned with early-recognition objectives.