Lingo3DMol
StoneWise AI Drug Design
Pocket-based 3D molecule generation combining language model token prediction with geometric deep learning for 3D coordinate generation. Published in Nature Machine Intelligence 2024.
Best For
Generating drug-like molecules with 3D coordinates directly inside target pockets
License
Open Source (check repo)
Strengths
- +Language model + 3D geometry hybrid
- +Nature Machine Intelligence publication
- +Pocket-conditioned generation
Limitations
- −Requires well-defined binding pocket
- −Generated 3D coordinates need refinement
- −Smaller community than DiffSBDD
R&D Pipeline Coverage
Related Tools
DiffSBDD
Schneuing et al. (Cambridge / Microsoft / VantAI)
Equivariant diffusion model for structure-based drug design that generates novel 3D molecules directly inside protein binding pockets. Published in Nature Computational Science 2024.
PocketFlow
PocketFlow Team
Flow-based generative model that creates novel small molecule ligands for a target binding pocket. Generates hundreds of candidates in minutes.
REINVENT4
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RL + transformer platform for de novo small molecule design. Supports scaffold decoration, R-group replacement, linker design, and multi-parameter optimization.
More in Generative Design
RFdiffusion
Baker Lab / IPD (University of Washington)
Diffusion-based generative model for de novo protein backbone design. Generates novel protein structures conditioned on binding targets, symmetry, or functional sites.
RFdiffusion2
Baker Lab / IPD (University of Washington)
Successor to RFdiffusion using flow matching. Designs enzymes directly from active site geometry (theozyme) specifications.
ProteinMPNN
Baker Lab / IPD (University of Washington)
Inverse folding model: generates amino acid sequences predicted to fold into a target 3D backbone structure. Standard component of all modern protein design pipelines.
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