Generation of 3D Molecules in Pockets via Language Model
This addresses the challenge of producing realistic 3D molecular structures in drug discovery, representing an incremental improvement over existing generative models.
The paper tackled the problem of generating 3D molecules for drug design by introducing Lingo3DMol, a method combining language models and geometric deep learning, which outperformed state-of-the-art methods on metrics like drug-likeness and binding mode using the DUD-E dataset.
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.