  Fragment-based drug discovery is an effective strategy for discovering drug
candidates in the vast chemical space, and has been widely employed in
molecular generative models. However, many existing fragment extraction methods
in such models do not take the target chemical properties into account or rely
on heuristic rules. Additionally, the existing fragment-based generative models
cannot update the fragment vocabulary with goal-aware fragments newly
discovered during the generation. To this end, we propose a molecular
generative framework for drug discovery, named Goal-aware fragment Extraction,
Assembly, and Modification (GEAM). GEAM consists of three modules, each
responsible for goal-aware fragment extraction, fragment assembly, and fragment
modification. The fragment extraction module identifies important fragments
contributing to the desired target properties with the information bottleneck
principle, thereby constructing an effective goal-aware fragment vocabulary.
Moreover, GEAM can explore beyond the initial vocabulary with the fragment
modification module, and the exploration is further enhanced through the
dynamic goal-aware vocabulary update. We experimentally demonstrate that GEAM
effectively discovers drug candidates through the generative cycle of the three
modules in various drug discovery tasks. Our code is available at
https://github.com/SeulLee05/GEAM.
