Structure generators are widely used in de novo design studies and their performance substantially influences an outcome. Approaches based on deep learning models and conventional atom-based approaches may result in invalid structures and did not address their synthetic feasibility issues. Conventional reaction-based approaches result in synthetically feasible compounds but novelty and diversity of generated compounds may be limited. Fragment-based approaches can provide better novelty and

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... sity of generated compounds but the issue of synthetic complexity of generated structure was not explicitly addressed before. Here, we developed a new fragment-based approach which results in chemically valid structures by design and gives flexible control over diversity, novelty, synthetic complexity and chemotypes of generated compounds. The approach was implemented as an open-source Python module.