Here, we present an open-access generative deep learning framework for creating virtual libraries of structurally novel and diverse molecules for project-tailored applications in drug discovery and related areas. The computational framework consists of an optimized chemical language model for designing new molecules that populate designated target areas in chemical space. We analyzed the suitability of chemical language models of both synthetic molecules and natural products to enrich libraries with desired characteristics (physicochemical properties, structural diversity and novelty) similar to those of screening compound libraries .