EvoDiff
MicrosoftVersion: 1
Key capabilities
About this model
EvoDiff can unconditionally sample diverse structurally-plausible proteins, generate intrinsically disordered regions, and scaffold structural motifs using only sequence information, challenging a paradigm in structure-based protein design.Key model capabilities
Below are several use cases for EvoDiff. Currently, Azure AI Foundry supports unconditional or conditional design with EvoDiff-Seq. To use EvoDiff-MSA, we point you to our github repository for more information.- Unconditional generation with EvoDiff-Seq or EvoDiff-MSA(https://github.com/microsoft/evodiff/blob/main/README.md#unconditional-generation-with-evodiff-msa )
- Conditional sequence generation
- Evolution-guided protein generation with EvoDiff-MSA
- Generating intrinsically disordered regions with EvoDiff-Seq and EvoDiff-MSA
- Scaffolding functional motifs with EvoDiff-Seq and EvoDiff-MSA
See Responsible AI for additional considerations for responsible use.
Key use cases
Below are several use cases for EvoDiff. Currently, Azure AI Foundry supports unconditional or conditional design with EvoDiff-Seq. To use EvoDiff-MSA, we point you to our github repository for more information.Out of scope use cases
This model is intended for use on protein sequences. It is not meant for natural language or other biological sequences, such as DNA sequences. This model will not generate sequences that are not proteins. This includes cases such as trying to generate other biological sequences, such as DNA sequences, or natural language. In other words, the model will perform best on data within the data distribution, which includes protein sequences and multiple sequence alignments (MSAs).Pricing is based on a number of factors, including deployment type and tokens used. See pricing details here.
Quick facts
Model providerMicrosoft
TypeProtein sequence generation
LifecycleGenerally available (GA)
Input typetext
Output typetext
Token limits4096 output
PricingView pricing