S4 was further tested on more challenging molecular entities than drug-like molecules. To this end, we evaluated its capacity to design natural products (NPs), which are invaluable sources of inspiration for medicinal chemistry . Compared to synthetic small molecules, NPs tend to possess more intricate molecular structures and ring systems, as well as a larger fraction of sp 3 -hybridized carbon atoms and chiral centers . These characteristics introduce longer SMILES sequences on average, with more long-range dependencies, and make natural products a challenging test case for CLMs . We trained the CLMs on large natural products (32,360 SMILES strings with length > 100, chosen to complement the previous analysis) from the COl-leCtion of Open Natural ProdUcTs (COCONUT) database . We then used the CLMs to design 102,400 SMILES strings de novo and computed the fraction of valid, unique, and novel designs (Table ). All CLMs can design natural products, with lower performance compared to drug-like molecules. S4 designs the highest number of valid molecules by approximately 6,000 to 12,000 molecules (7% to 13% better), and LSTM achieves the highest novelty by approximately 2,000 molecules (2%) over S4.