12 open-source projects similar to microsoft/foldingdiff, ranked by how many features they have in common. Compare stars, activity and what each one does to find the best Foldingdiff alternative.
This repository contains the source code accompanying the paper:
This repository provides the implementation code for our ICML paper. Below provides an illustration of the sampling process.
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…
Feynman-Kac guided protein design using RFdiffusion. This package implements particle filtering to optimize design objectives during the diffusion process.
B. Ni, D.L. Kaplan, M.J. Buehler, ForceGen: End-to-end de novo protein generation based on nonlinear mechanical unfolding responses using a language diffusion model. Sci. Adv. 10, eadl4000 (2024). DOI: 10.1126/sciadv.adl4000
This repository provides an interface for solving motif scaffolding problems with an unconditional diffusion model as a prior. It defines inverse problems for a variety of tasks and solves them by sampling the posterior via sequential Monte Carlo. This permits conditional sampling without…
This is the official code repository of the paper "TopoDiff: Improving Diffusion-Based Protein Backbone Generation with Global-Geometry-aware Latent Encoding".
FrameFlow is a SE(3) flow matching method for protein backbone generation and motif-scaffolding. The method is described in two papers:
Generation of Four Helix Backbones and Sequence Design via Deep Learning Pipeline
A text-to-protein backbone diffusion model. 1. Build Conda environment Run command: ` conda env create -n text2protein --file env.yaml `
Science: Built-in safeguards might stop AI from designing bioweapons - Nature Biotechnology: Watermarking generative AI for protein structure - Princeton AI Lab: Deep Dive Series: Building Biosecurity Safeguards into AI for Science