[upstream-sync] README.md from ai-boost/awesome-ai-for-science@03ff6200 [catalogue]

2026-07-10 08:29:23 +00:00
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 title: "Readme"
 task: ""
 lineage_type: import
-upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/230e8724/README.md
-upstream_sha: 230e8724
+upstream_source: https://github.com/ai-boost/awesome-ai-for-science/blob/03ff6200/README.md
+upstream_sha: 03ff6200
 imported_at: 2026-07-10
 prompt_class: catalogue
 upstream_changes: accepted
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 - [Chroma](https://github.com/generatebio/chroma) - Generative model for programmable protein design using diffusion modeling, equivariant graph neural networks, and conditional random fields to efficiently sample diverse all-atom structures; supports conditional generation via composable conditioners for substructure, symmetry, shape, and neural-network predictions; validated crystallographically (Generate Biomedicines, Nature 2023)
 - [EvoDiff](https://github.com/microsoft/evodiff) - Discrete diffusion framework for generative protein sequence design over evolutionary-scale databases, supporting unconditional generation, evolutionary-guided conditional design, motif scaffolding, and intrinsically disordered region generation through order-agnostic autoregressive diffusion, enabling sequence-only protein design without structural priors (Microsoft Research, Nature Communications 2024)
 - [DISCO](https://github.com/DISCO-design/DISCO) - General multimodal protein design framework enabling DNA-encoding of chemistry for programmable enzyme design and diverse protein generation through diffusion-based generative modeling (190+ stars, Apache 2.0, 2026)
- [SwitchCraft](https://github.com/bjing2016/switchcraft) - Programmatic framework for designing state-switching proteins via backpropagation through compositional design constraints parameterized by structure prediction models; enables de novo design of allosteric regulators and fluorescent biosensors for arbitrary small-molecule analytes (79+ stars, MIT License, ICML 2026)
 - [RFdiffusion3](https://github.com/RosettaCommons/RFdiffusion) - Latest RFdiffusion for protein structure design with 10× speedup and atom-level precision (December 2025)
 - [RFantibody](https://github.com/RosettaCommons/RFantibody) - Structure-based de novo antibody design pipeline built on RFdiffusion for computational generation of target-specific antibodies (RosettaCommons, 2025)
 - [IgGM](https://github.com/TencentAI4S/IgGM) - Generative foundation model for functional antibody and nanobody design, supporting de novo generation, affinity maturation, inverse design, structure prediction, and humanization (Tencent AI4S, ICLR 2025)