Generate protein structures from specified lengths and seeds
Image to 3D with DPT + 3D Point Cloud
Create a dynamic and colorful 3D scene with random objects and lights
Create a dynamic 3D scene with lights and knots
Render beautiful graphics with Moondream WebGPU
Generate 3D content from images or text
Create a 3D model from an image in ~10 seconds!
Generate 3D recursive polygons and math functions
Generate interactive 3D torus knots in a virtual environment
Sparse-view SFM-free Gaussian Splatting in Seconds
create 3d-gltf face-mesh from image with mediapipe
Create a dynamic 3D scene with random colorful knots
Generate a 3D mesh model from an image
Foldingdiff is a cutting-edge tool in the field of 3D Modeling, specifically designed to generate protein structures from specified lengths and seeds. It leverages advanced algorithms to predict and visualize protein folding, making it a valuable resource for researchers and scientists studying protein structures and their functions.
• Protein Structure Prediction: Generate accurate 3D protein structures from sequence lengths and seeds. • Customizable Inputs: Specify precise lengths and seeds for tailored structure generation. • High-Precision Modeling: Utilizes state-of-the-art algorithms for accurate folding predictions. • Visual Feedback: Provides detailed visual representation of generated structures for analysis. • User-Friendly Interface: Streamlined workflow for easy input and output handling.
What expertise is required to use foldingdiff?
Foldingdiff is designed for researchers and scientists with a background in bioinformatics or structural biology. Basic knowledge of protein structures and sequence analysis is recommended.
Can foldingdiff handle multiple sequences at once?
Currently, foldingdiff processes one sequence at a time. For batch processing, consider scripting or automating the tool using its API.
Is foldingdiff limited to protein structures?
Foldingdiff is primarily designed for protein structure prediction. However, its core algorithms can be adapted for similar tasks with appropriate modifications.