Add EvoDiff to Protein Structure Prediction and Design

README.md

@@ -356,6 +356,7 @@ Browse and search the resources via the [GitHub Pages UI](https://inoue0426.gith
 - [RoseTTAFold](https://github.com/RosettaCommons/RoseTTAFold) — Three-track neural network for protein structure prediction.
 - [OpenFold](https://github.com/aqlaboratory/openfold) — Trainable, memory-efficient open-source reproduction of AlphaFold2 enabling custom protein structure prediction workflows.
 - [SaProt](https://github.com/westlake-reup/SaProt) — Structure-aware protein language model using structure-aware tokens that encode both sequence and backbone geometry for improved function prediction.
+- [EvoDiff](https://github.com/microsoft/evodiff) — Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ [paper-2023](https://www.biorxiv.org/content/10.1101/2023.09.11.556673v1) ]
 
 #### Multi-Modal Foundation Models
 

data/resources.csv

@@ -143,6 +143,7 @@ enformer,Enformer,model,https://github.com/deepmind/deepmind-research/tree/maste
 esm3,ESM3,model,https://github.com/evolutionaryscale/esm,"Multimodal protein language model that jointly reasons over sequence, structure, and function for generative protein design and engineering.",foundation-models|protein-foundation-models,,,,,False,,
 esmfold,ESMFold,model,https://github.com/facebookresearch/esm,Fast protein structure prediction using language model embeddings.,foundation-models|protein-foundation-models,,,,,False,,
 evo,Evo,model,https://github.com/evo-design/evo,Long-context genomic foundation model (up to 1M tokens).,foundation-models|genomics-foundation-models,,,,,False,,
+evodiff,EvoDiff,model,https://github.com/microsoft/evodiff,"Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ [paper-2023](https://www.biorxiv.org/content/10.1101/2023.09.11.556673v1) ]",foundation-models|protein-foundation-models,,,,,False,,
 evolutionary_scale_modeling_esm,Evolutionary Scale Modeling (ESM),model,https://github.com/facebookresearch/esm,Protein embeddings.,foundation-models|protein-foundation-models,,,,,False,,
 gears,GEARS,model,https://github.com/snap-stanford/GEARS,Graph-based model for predicting transcriptional responses to single and combinatorial genetic perturbations using biological priors.,foundation-models|single-cell-foundation-models,,,,,False,,
 genecompass,GeneCompass,model,https://github.com/xCompass-AI/GeneCompass,Large-scale foundation model integrating DNA regulatory sequences and single-cell transcriptomics from 120M+ cells across multiple species for gene regulation prediction.,foundation-models|single-cell-foundation-models,,,,,False,,

data/resources.json

@@ -2063,6 +2063,21 @@
     "organism": [],
     "api": false
   },
+  {
+    "id": "evodiff",
+    "name": "EvoDiff",
+    "type": "model",
+    "url": "https://github.com/microsoft/evodiff",
+    "description": "Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ [paper-2023](https://www.biorxiv.org/content/10.1101/2023.09.11.556673v1) ]",
+    "tags": [
+      "foundation-models",
+      "protein-foundation-models"
+    ],
+    "tasks": [],
+    "modalities": [],
+    "organism": [],
+    "api": false
+  },
   {
     "id": "evolutionary_scale_modeling_esm",
     "name": "Evolutionary Scale Modeling (ESM)",

data/resources.yml

@@ -1599,6 +1599,17 @@ resources:
     organism: []
     api: false
 
+  - id: evodiff
+    name: "EvoDiff"
+    type: model
+    url: https://github.com/microsoft/evodiff
+    description: "Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ [paper-2023](https://www.biorxiv.org/content/10.1101/2023.09.11.556673v1) ]"
+    tags: [foundation-models, protein-foundation-models]
+    tasks: []
+    modalities: []
+    organism: []
+    api: false
+
   - id: evolutionary_scale_modeling_esm
     name: "Evolutionary Scale Modeling (ESM)"
     type: model

docs/data/resources.json

@@ -2063,6 +2063,21 @@
     "organism": [],
     "api": false
   },
+  {
+    "id": "evodiff",
+    "name": "EvoDiff",
+    "type": "model",
+    "url": "https://github.com/microsoft/evodiff",
+    "description": "Discrete diffusion framework for protein sequence generation trained on evolutionary-scale data, supporting unconditional generation, disordered region design, and functional motif scaffolding. [ [paper-2023](https://www.biorxiv.org/content/10.1101/2023.09.11.556673v1) ]",
+    "tags": [
+      "foundation-models",
+      "protein-foundation-models"
+    ],
+    "tasks": [],
+    "modalities": [],
+    "organism": [],
+    "api": false
+  },
   {
     "id": "evolutionary_scale_modeling_esm",
     "name": "Evolutionary Scale Modeling (ESM)",