We evaluated S4 for its ability to learn from and generate drug-like molecules and natural products in an array of tasks, and in terms of multiple molecular properties. LSTMs and Generative Pretrained Transformers (GPTs) were used as benchmarks, since they are the de facto approaches in chemical language modeling for de novo design . Furthermore, LSTM (recurrent training and generation) and GPT (holistic training and generation) constitute the ideal benchmarks for S4, due to S4's dual formulation (convolution during training and recurrence during generation), which allows inspecting the effect of each of these aspects on the overall performance. Finally, the prospective de novo design of putative MAPK1 inhibitors, corroborated by molecular dynamics simulations, was performed to test the potential of S4 in real-world drug discovery scenarios.