The most effective genetic algorithm currently available for molecular optimisation is the graph-based genetic algorithm (GB-GA), which encodes candidate molecules by their molecular graphs. This encoding allows for the generation of novel molecules through mutations or crossovers acting directly on the molecular graphs from within the existing population. The initial set of candidate molecules for GB-GA is typically sourced from publicly available molecular datasets such as ZINC or ChEMBL . GB-GA is highly effective in straightforward optimisation problems. However, like other genetic algorithms, GB-GA faces difficulties when encountering low-performing valleys or local optima in chemical space due to collapse of molecular diversity in its evolutionary population .