Overview
AlphaFold2 is a protein structure prediction model from Google DeepMind. AlphaFold2 demonstrates state-of-the-art performance at predicting protein structure form amino acid sequence, besting all other submissions on the Critical Assessment of protein Structure Prediction (CASP).
The NVIDIA AlphaFold2 NIM can:
Predict protein structure given a protein sequence.
Predict a multiple sequence alignment (MSA) for a given sequence against a series of protein sequence databases.
Predict a protein structure given a pre-computed MSA of the sequence against protein sequence databases.
For more information about AlphaFold2, see the AlphaFold2 paper in Nature. If you use this NIM or AlphaFold2, make sure to cite the paper:
Jumper, J., Evans, R., Pritzel, A. et al. Highly accurate protein structure prediction with AlphaFold. Nature 596, 583–589 (2021). https://doi.org/10.1038/s41586-021-03819-2
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 allow 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.
The AlphaFold2 NIM provides a fast, accurate model behind a consistent API for predicting protein structure. As part of the broader NVIDIA NIM Ecosystem, AlphaFold2 can be used in conjunction with other NIMs to build pipelines that generate and assess the structure and function of entirely new proteins and small molecules.
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