Overview
RFdiffusion (RoseTTAFold Diffusion) is a powerful deep learning model designed to generate novel protein structures and complexes.
This network employs a diffusion-based approach to progressively refine protein structures, allowing for the creation of diverse and high-quality 3D protein models.
The input to the neural network is a set of constraints or specifications in various formats, including partial protein structures in PDB format, and the output is the generated 3D protein structure in PDB format.
RFdiffusion is one of many NIMs that you can apply to tasks in biosciences and drug discovery. NIMs make it easy to chain models together to develop a complete in silico drug discovery pipeline.
For example, you can use a RFdiffusion NIM as a first step to generate 3D structure a binder, and subsequently use ProteinMPNN NIM to determine possible amino acid sequence that fold into this 3D structure.
A more detailed description of the model can be found in the published RFdiffusion paper.1
In general, NIMs offer a simple and easy-to-deploy route for self-hosted AI applications. Two major advantages that NIMs offer for system administrators and developers are:
Increased productivity: NIMs enable developers to build generative AI applications quickly, in minutes rather than weeks, by providing a standardized way to add AI capabilities to their applications.
Simplified deployment: NIMs provide containers that can be easily deployed on various platforms, including clouds, data centers, or workstations, making it convenient for developers to test and deploy their applications.
In the context of protein design and drug development, these advantages can:
Accelerate lead optimization: Researchers can use NIMs to accelerate the lead optimization process by quickly generating and testing multiple molecular structures, enabling them to identify potential leads more efficiently.
Streamline data analysis: Researchers can use NIMs to analyze large datasets generated during the drug discovery process, such as molecular dynamics simulations or high-throughput screening data, to identify patterns and trends that can inform the development of new drugs.
Improve collaboration: NIMs can facilitate collaboration among researchers by providing a standardized platform for sharing and integrating AI models, enabling teams to work together more effectively and efficiently.
Enhance predictive modeling: Researchers can use NIMs to develop and deploy predictive models that can accurately predict the properties and behavior of molecules, such as their binding affinity or toxicity, enabling them to make more informed decisions during the drug development process.