Generate 3D molecular models from SMILES strings
Generate 3D content from images or text
Convert images to 3D models
Create recursive 3D polygons and mathematical surfaces
Create and explore 3D recursive polygons and math functions
Create 3D scenes with recursive polygons and math functions
Generate protein structures from specified lengths and seeds
Scalable and Versatile 3D Generation from images
Create a 3D model from an image using depth mapping
Generate 3D models from text prompts
Generate 3D models from images
generate any 3d looking art in seconds.
create 3d-gltf face-mesh from image with mediapipe
SMILES_RDKit_Py3DMOL_FORK is a tool designed to generate 3D molecular models from SMILES strings. It leverages the power of RDKit for chemical structure processing and Py3DMOL for 3D visualization, making it a robust solution for chemists and researchers to convert 2D chemical representations into interactive 3D models.
• SMILES String Conversion: Easily convert SMILES strings into 3D molecular structures. • 3D Visualization: Generate high-quality 3D molecular models using Py3DMOL. • Chemical Manipulation: Utilize RDKit's capabilities for molecular structure manipulation and analysis. • Customizable Visualizations: Adjust visual properties such as colors, bond styles, and atom labels. • Cross-Platform Compatibility: Run the tool on Windows, macOS, and Linux systems. • Integration Ready: Easily integrate with other cheminformatics pipelines and workflows.
pip install SMILES_RDKit_Py3DMOL_FORK.from SMILES_RDKit_Py3DMOL_FORK import smiles_to_3d
smiles_to_3d function to generate a 3D model:
molecule = smiles_to_3d("CC(=O)Nc1ccc(cc1)C(=O)N") # Example SMILES string
molecule.visualize()
What is the input format for SMILES strings?
The input should be a valid SMILES string, such as "CC(=O)Nc1ccc(cc1)C(=O)N".
Can I customize the appearance of the 3D model?
Yes, you can customize colors, bond styles, and atom labels using Py3DMOL's styling options.
Is the tool compatible with all operating systems?
Yes, SMILES_RDKit_Py3DMOL_FORK is designed to work on Windows, macOS, and Linux.