bio-generative-design skillA
bio-generative-design is agent-read markdown (skill) from pku-yuangroup/openai4s: Designs novel molecules using REINVENT 4 (de novo, scaffold decoration, linker design, R-group, molecular optimization), MolMIM, Diffusion-based generators (DiGress, DiffSMol), and JT-VAE with explicit handling of multi-parameter optimization (MPO), goal-directed scoring functions, transfer/reinforcement/curriculum learning, synthetic accessibility scoring, and chemical space exploration vs exploitation. Use when designing new chemical matter against a target, decorating a scaffold, linking frag.
Indexed from public GitHub and served as immutable, content-addressed versions. Install it pinned to an exact SHA-256 with the mdr CLI, and every file is verified against the hash recorded here before it reaches your agent. The deterministic audit below grades the latest version, and the same file always earns the same grade.
What the file says
## 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>` then `help(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… …
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How to install
mdr add pku-yuangroup/openai4s/bio-generative-design@git:20260821.2d1b678mdr add pku-yuangroup/openai4s/bio-generative-design@sha256:b0fc2e9b4dcf266bPin to a label to follow the author's releases, or to a sha256 to freeze the exact bytes forever. Either way the resolved hash is written to mdr.lock, and mdr install reproduces it on any machine.
[](https://markdownregistry.com/a/art_5b6bn3nev3ikjs3r)
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Source
pku-yuangroup/openai4s · 586 stars · license MIT · pushed 2026-09-23 · branch main
API
GET https://markdownregistry.com/api/v1/artifacts/art_5b6bn3nev3ikjs3r GET https://markdownregistry.com/api/v1/resolve?ref=pku-yuangroup/openai4s/bio-generative-design GET https://markdownregistry.com/api/v1/blob/b0fc2e9b4dcf266ba33e4d338a1d8418924fa39a77976654ef7a4dde836cb1e9
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