update_name

.gitignore

@@ -0,0 +1,161 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+
+.DS_Store
+.idea/
+__pycache__/
+dgd/configs/__pycache__/
+egnn/__pycache__/
+equivariant_diffusion/__pycache__/
+outputs/
+archives/qm9/__pycache__/
+archives/qm9/data_utils/__pycache__/
+archives/qm9/data_utils/prepare/__pycache__/
+archives/qm9/property_prediction/__pycache__/
+archives/*
+.env
+results/*.ckpt
+results/qm9_molecules_h
+results/qm9_molecules_noh
+dgd/analysis/orca/orca
+results/*
+ggg_data/
+ggg_utils/
+saved_models
+src/analysis/orca/orca
+src/analysis/orca/tmp_XMYAR426.txt
+
+
+# New
+archive.zip
+logs/
+generated/
+data/processed/

README.md

@@ -1,9 +1,9 @@
-Inverse Molecular Design with Multi-Conditional Diffusion Guidance
+Graph Diffusion Transformer for Multi-Conditional Molecular Generation
 ================================================================
 
 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">
@@ -16,7 +16,7 @@ All dependencies are specified in the `requirements.txt` file.
 
 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).
 

configs/config.yaml

@@ -1,5 +1,5 @@
 general:
-    name: 'MCD'
+    name: 'graph_dit'
     wandb: 'disabled' 
     gpus: 1
     resume: null
@@ -14,11 +14,11 @@ general:
     final_model_samples_to_save: 20
     final_model_chains_to_save: 1
     enable_progress_bar: False
-    save_model: False
+    save_model: True
 model:
     type: 'discrete'
     transition: 'marginal'                  
-    model: 'MCD'
+    model: 'graph_dit'
     diffusion_steps: 500
     diffusion_noise_schedule: 'cosine'
     guide_scale: 2

graph_dit/diffusion_model.py

@@ -12,7 +12,7 @@ from metrics.train_loss import TrainLossDiscrete
 from metrics.abstract_metrics import SumExceptBatchMetric, SumExceptBatchKL, NLL
 import utils
 
-class MCD(pl.LightningModule):
+class Graph_DiT(pl.LightningModule):
     def __init__(self, cfg, dataset_infos, train_metrics, sampling_metrics, visualization_tools):
         super().__init__()
         self.save_hyperparameters(ignore=['train_metrics', 'sampling_metrics'])
@@ -174,7 +174,6 @@ class MCD(pl.LightningModule):
     def validation_step(self, data, i):
         data_x = F.one_hot(data.x, num_classes=118).float()[:, self.active_index]
         data_edge_attr = F.one_hot(data.edge_attr, num_classes=5).float()
-
         dense_data, node_mask = utils.to_dense(data_x, data.edge_index, data_edge_attr, data.batch, self.max_n_nodes)
         dense_data = dense_data.mask(node_mask)
         noisy_data = self.apply_noise(dense_data.X, dense_data.E, data.y, node_mask)
@@ -281,7 +280,6 @@ class MCD(pl.LightningModule):
         chains_left_to_save = self.cfg.general.final_model_chains_to_save
 
         samples, all_ys, batch_id = [], [], 0
-
         test_y_collection = torch.cat(self.test_y_collection, dim=0)
         num_examples = test_y_collection.size(0)
         if self.cfg.general.final_model_samples_to_generate > num_examples:

graph_dit/main.py

@@ -9,7 +9,7 @@ from pytorch_lightning import Trainer
 
 import utils
 from datasets import dataset
-from diffusion_model import MCD
+from diffusion_model import Graph_DiT
 from metrics.molecular_metrics_train import TrainMolecularMetricsDiscrete
 from metrics.molecular_metrics_sampling import SamplingMolecularMetrics
 
@@ -36,7 +36,7 @@ def get_resume(cfg, model_kwargs):
     name = cfg.general.name + "_resume"
     resume = cfg.general.test_only
     batch_size = cfg.train.batch_size
-    model = MCD.load_from_checkpoint(resume, **model_kwargs)
+    model = Graph_DiT.load_from_checkpoint(resume, **model_kwargs)
     cfg = model.cfg
     cfg.general.test_only = resume
     cfg.general.name = name
@@ -54,7 +54,7 @@ def get_resume_adaptive(cfg, model_kwargs):
     resume_path = os.path.join(root_dir, cfg.general.resume)
 
     if cfg.model.type == "discrete":
-        model = MCD.load_from_checkpoint(
+        model = Graph_DiT.load_from_checkpoint(
             resume_path, **model_kwargs
         )
     else:
@@ -73,7 +73,7 @@ def get_resume_adaptive(cfg, model_kwargs):
 
 
 @hydra.main(
-    version_base="1.1", config_path="../configs", config_name="config_dev"
+    version_base="1.1", config_path="../configs", config_name="config"
 )
 def main(cfg: DictConfig):
 
@@ -106,7 +106,7 @@ def main(cfg: DictConfig):
         cfg, _ = get_resume_adaptive(cfg, model_kwargs)
         os.chdir(cfg.general.resume.split("checkpoints")[0])
 
-    model = MCD(cfg=cfg, **model_kwargs)
+    model = Graph_DiT(cfg=cfg, **model_kwargs)
     trainer = Trainer(
         gradient_clip_val=cfg.train.clip_grad,
         accelerator="gpu"

graph_dit/models/transformer.py

@@ -44,7 +44,7 @@ class Denoiser(nn.Module):
             ]
         )
 
-        self.decoder = Decoder(
+        self.out_layer = OutLayer(
             max_n_nodes=max_n_nodes,
             hidden_size=hidden_size,
             atom_type=Xdim,
@@ -73,7 +73,7 @@ class Denoiser(nn.Module):
 
         for block in self.encoders :
             _constant_init(block.adaLN_modulation[0], 0)
-        _constant_init(self.decoder.adaLN_modulation[0], 0)
+        _constant_init(self.out_layer.adaLN_modulation[0], 0)
 
     def forward(self, x, e, node_mask, y, t, unconditioned):
         
@@ -99,7 +99,7 @@ class Denoiser(nn.Module):
             x = block(x, c, node_mask)
 
         # X: B * N * dx, E: B * N * N * de
-        X, E, y = self.decoder(x, x_in, e_in, c, t, node_mask)
+        X, E, y = self.out_layer(x, x_in, e_in, c, t, node_mask)
         return utils.PlaceHolder(X=X, E=E, y=y).mask(node_mask)
 
 
@@ -140,8 +140,8 @@ class SELayer(nn.Module):
         return x
 
 
-class Decoder(nn.Module):
-    # Structure Decoder
+class OutLayer(nn.Module):
+    # Structure Output Layer
     def __init__(self, max_n_nodes, hidden_size, atom_type, bond_type, mlp_ratio, num_heads=None):
         super().__init__()
         self.atom_type = atom_type