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gang liu
2024-05-25 15:32:36 -04:00
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Inverse Molecular Design with Multi-Conditional Diffusion Guidance
Graph Diffusion Transformer for Multi-Conditional Molecular Generation
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Paper: https://arxiv.org/abs/2401.13858
This is the code for MCD: a Multi-Conditional Diffusion Model for inverse small molecule and polymer designs and generations. The denoising model architecture in `mcd/models` looks like:
This is the code for Graph DiT. The denoising model architecture in `graph_dit/models` looks like:
<div style="display: flex;" markdown="1">
<img src="asset/reverse.png" style="width: 45%;" alt="Description of the first image">
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This code was developed and tested with Python 3.9.16, PyTorch 2.0.0, and PyG 2.3.0, Pytorch-lightning 2.0.1.
For molecular generation evaluation, we should first install rdkit:
For molecular generation evaluation, we should first install rdkit.
Then `fcd_torch`: `pip install fcd_torch` (https://github.com/insilicomedicine/fcd_torch).