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  1. Home
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  4. EvoDiff
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EvoDiff

Free generative AI for designing novel proteins — drug discovery breakthrough

Developed by Microsoft Research

Try Model
38M / 170M / 640MParams
YesAPI
stableStability
EvoDiffVersion
MITLicense
PyTorchFramework
YesRuns Local

Playground

Implementation Example

Example Prompt

user input
Generate 50 novel protein sequences of length 120 amino acids that fold into a beta-barrel structure (suitable for membrane proteins).

Model Output

model response
Returns 50 unique amino acid sequences (FASTA format) with predicted structural propensity for beta-barrel folding. Each can be validated through AlphaFold or ESMFold for confirmation, then synthesized for wet-lab testing — typical pipeline reduces months of design work to hours.

Examples

Real-World Applications

  • Novel enzyme design
  • therapeutic antibody generation
  • drug-target binders
  • vaccine candidate generation
  • industrial biocatalysts
  • synthetic biology engineering.

Docs

Model Intelligence & Architecture

What is EvoDiff?

EvoDiff is a generative diffusion model for protein sequences developed by Microsoft Research and released in September 2023. While AlphaFold predicts structures from sequences, EvoDiff designs new protein sequences from scratch using diffusion modeling — making it the inverse complement to structure prediction.

It's released under the MIT license, free for any commercial use including biotech and pharmaceutical research.

Why EvoDiff Is Trending in 2026

As generative protein design revolutionizes drug discovery, EvoDiff has become a key tool for designing novel enzymes, antibodies, and therapeutic proteins. Combined with AlphaFold for structure validation, it enables a complete design → predict → test loop for protein engineering.

Key Features and Capabilities

EvoDiff supports unconditional protein sequence generation, sequence inpainting (filling missing regions), structure-conditioned design, evolutionary-aware diffusion, and rapid sampling at scale.

Who Should Use EvoDiff?

EvoDiff is built for biotech researchers, pharmaceutical R&D teams, synthetic biologists, drug-discovery engineers, and enzyme designers.

Top Use Cases

Real-world applications include novel enzyme design, therapeutic antibody generation, drug-target binder design, vaccine candidate generation, biocatalyst design for industrial chemistry, and protein engineering for synthetic biology.

Where Can You Run It?

EvoDiff runs on any system with PyTorch. The model is relatively lightweight and runs on a single consumer GPU.

How to Use EvoDiff (Quick Start)

Clone: git clone https://github.com/microsoft/evodiff. Install dependencies, then generate sequences: python generate.py --model EvoDiff_OAR_640M --num_seqs 100 --seq_length 100. Validate generated sequences with AlphaFold for structure.

When Should You Choose EvoDiff?

Choose EvoDiff when you need novel protein sequence generation with controllable conditioning. For structure-driven design, also use ProteinMPNN or RFdiffusion alongside it.

Pricing

EvoDiff is completely free under MIT license.

Pros and Cons

Pros: ✔ MIT license ✔ Pioneering diffusion-based protein design ✔ Inpainting capabilities ✔ Evolutionary-aware ✔ Microsoft research backing ✔ Complements AlphaFold

Cons: ✘ Specialized for biology only ✘ Generated proteins need lab validation ✘ Smaller community than AlphaFold ✘ Requires biology expertise

Final Verdict

EvoDiff is a leading open-source generative protein design AI in 2026 — essential for modern drug discovery. Discover more bio AI at FreeAPIHub.com.

Evaluation

Advantages & Limitations

Advantages
  • ✓ MIT license
  • ✓ Pioneering diffusion-based protein design
  • ✓ Inpainting capabilities
  • ✓ Evolutionary-aware training
  • ✓ Microsoft research backing
  • ✓ Complements AlphaFold
Limitations
  • ✗ Specialized for biology only
  • ✗ Generated proteins need lab validation
  • ✗ Smaller community than AlphaFold
  • ✗ Requires biology expertise

Important Notice

Verify Before You Decide

Last verified · Apr 29, 2026

The details on this page — including pricing, features, and availability — are based on our last review and may not reflect the provider's current offering. Providers update their products frequently, sometimes without prior notice.

What may have changed

Pricing Plans
Features & Limits
Availability
Terms & Policies

Always visit the official provider website to confirm the latest pricing, terms, and feature availability before subscribing or integrating.

Check official site

External Resources

Try the Model Official Website Source Code

Technical Details

Architecture
Discrete diffusion model on amino acid sequences
Stability
stable
Framework
PyTorch
License
MIT
Release Date
2023-09-12
Signup Required
No
API Available
Yes
Runs Locally
Yes

Rate Limits

No limits self-hosted

Pricing

Completely free under MIT license

Best For

Biotech and pharma teams designing novel proteins for drugs and enzymes

Alternative To

ProteinMPNN, RFdiffusion, ESMFold (for prediction)

Compare With

evodiff vs proteinmpnnevodiff vs rfdiffusionevodiff vs alphafoldfree protein design aigenerative protein ai

Tags

#Evodiff#Protein Design#Drug Discovery#Bioinformatics#Microsoft Research#generative-ai

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