bio-generative-design
Version Compatibility
Reference examples tested with: REINVENT 4.0+, RDKit 2024.09+, PyTorch 2.1+, MolMIM (NVIDIA BioNeMo), chemprop 2.0+.
Before using code patterns, verify installed versions match. If versions differ:
- Python:
pip show <package>thenhelp(module.function)to check signatures
If code throws ImportError, AttributeError, or TypeError, introspect the installed package and adapt the example to match the actual API rather than retrying.
Generative Molecular Design
Generate novel molecules biased toward desired properties using deep generative models. REINVENT 4 (Loeffler et al. 2024, AstraZeneca) provides four generator families: Reinvent (de novo), Libinvent (scaffold decoration and library design), Linkinvent (linker design), and Mol2Mol (similarity-constrained molecular optimization). These support design tasks including R-group replacement and scaffold hopping and can be used with transfer learning, reinforcement learning, and curriculum learning. For specific niches: MolMIM (NVIDIA BioNeMo) for latent-space property optimization, DiffSMol / DiGress for diffusion-based generation, and JT-VAE for latent-space optimization. The art of generative design is in the scoring function: poorly-designed scoring rewards uninteresting molecules, while well-designed scoring captures both activity and developability.
For QSAR/scoring models that feed generative design, see chemoinformatics/qsar-modeling. For synthetic feasibility, see chemoinformatics/retrosynthesis. For library enumeration as alternative, see chemoinformatics/reaction-enumeration.