The construction of a virtual library
(VL) consisting of novel
molecules based on structure–activity relationships is crucial
for lead optimization in rational drug design. In this study, we propose
a novel scaffold-retained structure generator, EMPIRE (Exhaustive
Molecular library Production In a scaffold-REtained manner), to create
novel molecules in an arbitrary chemical space. By combining a deep
learning model-based generator and a building block-based generator,
the proposed method efficiently provides a VL consisting of molecules
that retain the input scaffold and contain unique arbitrary substructures.
The proposed method enables us to construct rational VLs located in
unexplored chemical spaces containing molecules with unique skeletons
(e.g., bicyclo[1.1.1]pentane and cubane) or elements (e.g., boron
and silicon). We expect EMPIRE to contribute to efficient drug design
with unique substructures by virtual screening.