Genie 2
Diffusion model for scalable protein structure design with multi-motif scaffolding capabilities, achieving state-of-the-art designability, diversity, and novelty through SE(3)-equivariant attention and massive data augmentation (AlQuraishi Lab, 2024)
README
Genie 2: Designing and Scaffing Proteins at the Scale of the Structural Universe This repository provides the implementation code for our preprint, including training and inference code, as well as model weights. For the in-silico evaluation pipeline, which is used to assess the designability, diversity and novelty of our generated structures, we provide them in a seperate repository since it is independent of Genie 2 and could be applicable for evaluating other protein structure diffusion…
- Repository
- github.com/aqlaboratory/genie2
Source attribution
- Awesome AI for Science — github.com/aqlaboratory/genie2
- GitHub — github.com/aqlaboratory/genie2
Related resources
Deep learning library for Chemistry based on Tensorflow
Industrial-grade reinforcement-learning-based generative platform for de novo molecular design with transformer architectures, supporting multi-objective optimization, scaffold decoration, and curriculum learning (AstraZeneca MolecularAI, REINVENT 4, 2024)
General multimodal protein design framework enabling DNA-encoding of chemistry for programmable enzyme design and diverse protein generation through diffusion-based generative modeling (190+ stars, Apache 2.0, 2026)
Neural network-based cryo-EM heterogeneous reconstruction, modeling continuous 3D structure distributions from single-particle images, with CryoDRGN-ET extending to in-cell cryo-electron tomography (MIT CSAIL, Nature Methods 2021/2024)
Deep learning system for de novo design of high-affinity protein binders, achieving strong binding across diverse target classes including challenging intracellular proteins with significantly higher success rates than traditional wet-lab screening methods (Google DeepMind, Nature 2024)
AlphaFold 3 inference pipeline for unified biomolecular structure prediction of proteins, nucleic acids, small molecules, ions, and post-translational modifications (Google DeepMind, Nature 2024)