DISCO-Design/DISCO_benchmark_data

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license: apache-2.0
pretty_name: DISCO Benchmark Data
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<img src="assets/disco.png" alt="DISCO: Diffusion for Sequence-Structure Co-design" width="900"/>
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<img src="assets/carbene.gif" width="700"/>
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<a href="[https://arxiv.org/abs/2412.17762](https://arxiv.org/abs/XXXX.XXXXX)"><img src="https://img.shields.io/badge/arXiv-94133F?style=for-the-badge&logo=arxiv" alt="arXiv"/></a>
<a href="https://PLACEHOLDER_BLOG_URL"><img src="https://img.shields.io/badge/📝%20Blog-007A87?style=for-the-badge&logoColor=white" alt="Jupyter"/></a>
<a href="https://huggingface.co/superdiff/"><img src="https://img.shields.io/badge/github-repo-blue?style=for-the-badge&logo=github" alt="HF"/></a>
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DISCO (DIffusion for Sequence-structure CO-design) is a multimodal generative model that simultaneously co-designs protein sequences and 3D structures, conditioned on and co-folded with arbitrary biomolecules — including small-molecule ligands, DNA, and RNA. Unlike sequential pipelines that first generate a backbone and then apply inverse folding, DISCO generates both modalities jointly, enabling sequence-based objectives to inform structure generation and vice versa.

DISCO achieves state-of-the-art in silico performance in generating binders for diverse biomolecular targets with fine-grained property control. Applied to new-to-nature catalysis, DISCO was conditioned solely on reactive intermediates — without pre-specifying catalytic residues or relying on template scaffolds — to design diverse heme enzymes with novel active-site geometries. These enzymes catalyze new-to-nature carbene-transfer reactions, including alkene cyclopropanation, spirocyclopropanation, B–H and C(sp³)–H insertions, with top activities exceeding those of engineered enzymes. Random mutagenesis of a selected design further yielded a fourfold activity gain, indicating that the designed enzymes are evolvable.

This contains the raw data generated and used for benchmarking DISCO. We open source it here to aid benchmarking against DISCO on various in silico benchmarks.