  Generative models for molecules based on sequential line notation (e.g.
SMILES) or graph representation have attracted an increasing interest in the
field of structure-based drug design, but they struggle to capture important 3D
spatial interactions and often produce undesirable molecular structures. To
address these challenges, we introduce Lingo3DMol, a pocket-based 3D molecule
generation method that combines language models and geometric deep learning
technology. A new molecular representation, fragment-based SMILES with local
and global coordinates, was developed to assist the model in learning molecular
topologies and atomic spatial positions. Additionally, we trained a separate
noncovalent interaction predictor to provide essential binding pattern
information for the generative model. Lingo3DMol can efficiently traverse
drug-like chemical spaces, preventing the formation of unusual structures. The
Directory of Useful Decoys-Enhanced (DUD-E) dataset was used for evaluation.
Lingo3DMol outperformed state-of-the-art methods in terms of drug-likeness,
synthetic accessibility, pocket binding mode, and molecule generation speed.
