Designing molecules with desired properties from scratch is a 'needle in the haystack ' problem. The chemical universe -estimated to comprise up to 10 60 small molecules -remains largely uncharted. Generative deep learning offers unprecedented opportunities to explore the chemical universe in a time-and cost-efficient manner , by enabling the production of desirable molecules without the need for handcrafted design rules. In particular, chemical language models (CLMs) have yielded experimentallyvalidated bioactive designs and stood out as powerful molecular generators .