MolCraftDiffusion

A unified generative-AI framework for 3D molecular design.

MolCraftDiffusion streamlines the full pipeline — from training diffusion models to deploying them in data-driven computational chemistry workflows.

Workflow overview

GitHub arXiv DOI Weights Dataset Demo

Key Features

MolCraftDiffusion is built with modularity at its core, offering an all-in-one, systematic workflow entirely driven by a unified CLI and YAML configuration files.

  • Data Module — Preprocess, compile, and manage raw .xyz files into unified .db (ASE Database) pipelines, and annotate properties.

  • Training & Fine-Tuning Module — Flexibly train (or fine-tune) diffusion models, property regressors, and time-aware guidance models.

  • Generation & Guidance Module — Generate 3D molecules using a variety of guidance mechanisms:

    • Unconditional Generation: Generate 3D molecules without any specific constraints or guidance.

    • Property-Targeted Guidance: Steer generation toward desired properties using Classifier-Free Guidance (CFG), Gradient Guidance (GG), or a hybrid approach.

    • Structure-Guided Generation: Perform inpainting (scaffold decoration) and outpainting (fragment extension) with precise 3D geometric constraints.

  • Analysis & Evaluation Module — Assess the quality of generated 3D molecules. Includes tools for structural validity metrics, xTB geometry optimization, RMSD comparisons, and quantum-chemical property calculation/prediction.

Quick Start

# Train a diffusion model
MolCraftDiff train my_config

# Generate molecules
MolCraftDiff generate my_gen_config

# Analyse outputs
MolCraftDiff analyze metrics -i generated_molecules/

Contents

Getting Started

Tutorials

API Reference