Drug Discovery with Dynamic Goal-aware Fragments

Seul Lee, Seanie Lee, Kenji Kawaguchi, Sung Ju Hwang

Keywords: Drug discovery, Molecule generation, Fragment-based drug discovery

Abstract: Fragment-based drug discovery 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. We experimentally demonstrate that GEAM effectively discovers drug candidates through the generative cycle of the three modules in various drug discovery tasks.

Submission Number: 17