Duplicate from jannisborn/gt4sd-torchdrug

.gitattributes

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+*.arrow filter=lfs diff=lfs merge=lfs -text
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+*.bz2 filter=lfs diff=lfs merge=lfs -text
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.gitignore

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+__pycache__/

LICENSE

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+MIT License
+
+Copyright (c) 2022 Generative Toolkit 4 Scientific Discovery
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+---
+title: MoLeR
+emoji: 💡
+colorFrom: green
+colorTo: blue
+sdk: gradio
+sdk_version: 3.9.1
+app_file: app.py
+pinned: false
+python_version: 3.8.13
+pypi_version: 20.2.4
+duplicated_from: jannisborn/gt4sd-torchdrug
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import logging
+import pathlib
+
+import gradio as gr
+import pandas as pd
+from gt4sd.algorithms.generation.torchdrug import (
+    TorchDrugGenerator,
+    TorchDrugGCPN,
+    TorchDrugGraphAF,
+)
+
+from gt4sd.algorithms.registry import ApplicationsRegistry
+from utils import draw_grid_generate
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
+
+TITLE = "MoLeR"
+
+
+def run_inference(algorithm: str, algorithm_version: str, number_of_samples: int):
+
+    if algorithm == "GCPN":
+        config = TorchDrugGCPN(algorithm_version=algorithm_version)
+    elif algorithm == "GraphAF":
+        config = TorchDrugGraphAF(algorithm_version=algorithm_version)
+    else:
+        raise ValueError(f"Unsupported model {algorithm}.")
+
+    model = TorchDrugGenerator(configuration=config)
+    samples = list(model.sample(number_of_samples))
+
+    return draw_grid_generate(samples=samples, n_cols=5)
+
+
+if __name__ == "__main__":
+
+    # Preparation (retrieve all available algorithms)
+    all_algos = ApplicationsRegistry.list_available()
+    algos = [
+        x["algorithm_version"]
+        for x in list(filter(lambda x: "TorchDrug" in x["algorithm_name"], all_algos))
+    ]
+
+    # Load metadata
+    metadata_root = pathlib.Path(__file__).parent.joinpath("model_cards")
+
+    examples = pd.read_csv(metadata_root.joinpath("examples.csv"), header=None).fillna(
+        ""
+    )
+
+    with open(metadata_root.joinpath("article.md"), "r") as f:
+        article = f.read()
+    with open(metadata_root.joinpath("description.md"), "r") as f:
+        description = f.read()
+
+    demo = gr.Interface(
+        fn=run_inference,
+        title="TorchDrug (GCPN and GraphAF)",
+        inputs=[
+            gr.Dropdown(["GCPN", "GraphAF"], label="Algorithm", value="GCPN"),
+            gr.Dropdown(
+                list(set(algos)), label="Algorithm version", value="zinc250k_v0"
+            ),
+            gr.Slider(
+                minimum=1, maximum=50, value=10, label="Number of samples", step=1
+            ),
+        ],
+        outputs=gr.HTML(label="Output"),
+        article=article,
+        description=description,
+        examples=examples.values.tolist(),
+    )
+    demo.launch(debug=True, show_error=True)

model_cards/article.md

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+# Model documentation & parameters
+
+**Algorithm**: Which model to use (GCPN or GraphAF).
+
+**Algorithm Version**: Which model checkpoint to use (trained on different datasets).
+
+**Number of samples**: How many samples should be generated (between 1 and 50).
+
+
+# Model card -- GCPN
+
+**Model Details**: GCPN is a graph-based molecular generative model that can be optimized with RL for goal-directed graph generation.
+
+**Developers**: Jiaxuan You, Bowen Liu and co-authors from Stanford.
+
+**Distributors**: Code provided by TorchDrug developers, wrapped and distributed by GT4SD Team (2023) from IBM Research.
+
+**Model date**: Published in 2018.
+
+**Model version**: Models trained by GT4SD team on the tasks provided by TorchDrug repo [(see their tutorial)](https://torchdrug.ai/docs/tutorials/generation.html). 
+- **ZINC_250k**: 250,000 drug-like molecules with a maximum atom number of 38, taken from [ZINC](https://zinc.docking.org).
+- **QED**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high QED scores.
+- **pLogP**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high pLogP scores.
+
+**Model type**: A graph-based molecular generative model that can be optimized with RL for goal-directed graph generation.
+
+**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: Default parameters as provided in [(TorchDrug tutorial)](https://torchdrug.ai/docs/tutorials/generation.html).
+
+**Paper or other resource for more information**: [Graph Convolutional Policy Network for
+Goal-Directed Molecular Graph Generation (NeurIPS 2018)](https://proceedings.neurips.cc/paper/2018/file/d60678e8f2ba9c540798ebbde31177e8-Paper.pdf).
+
+**License**: TorchDrug: Apache-2.0 license.
+
+**Where to send questions or comments about the model**: Open an issue on [TorchDrug repository](https://github.com/DeepGraphLearning/torchdrug) or ask original authors.
+
+**Intended Use. Use cases that were envisioned during development**: Chemical research, in particular drug discovery.
+
+**Primary intended uses/users**: Researchers and computational chemists using the model for model comparison or research exploration purposes.
+
+**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
+
+**Factors**: Not applicable.
+
+**Metrics**: Validation loss on decoding correct molecules.
+
+**Datasets**: 250,000 drug-like molecules from [ZINC](https://zinc.docking.org) (with a maximum atom number of 38).
+
+**Ethical Considerations**: Unclear, please consult with original authors in case of questions.
+
+**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.
+
+Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
+
+## Citation
+
+```bib
+@article{you2018graph,
+  title={Graph convolutional policy network for goal-directed molecular graph generation},
+  author={You, Jiaxuan and Liu, Bowen and Ying, Zhitao and Pande, Vijay and Leskovec, Jure},
+  journal={Advances in neural information processing systems},
+  volume={31},
+  year={2018}
+}
+```
+
+
+# Model card -- GraphAF
+
+**Model Details**: GraphAF is a flow-based autoregressive graph molecular generative model that can be optimized with RL for goal-directed graph generation.
+
+**Developers**: Chence Shi, Minkai Xu and co-authors from Peking and Shanghai University and MILA.
+
+**Distributors**: Code provided by TorchDrug developers, wrapped and distributed by GT4SD Team (2023) from IBM Research.
+
+**Model date**: Published in 2020.
+
+**Model version**: Models trained by GT4SD team on the tasks provided by TorchDrug repo [(see their tutorial)](https://torchdrug.ai/docs/tutorials/generation.html). 
+- **ZINC_250k**: 250,000 drug-like molecules with a maximum atom number of 38, taken from [ZINC](https://zinc.docking.org).
+- **QED**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high QED scores.
+- **pLogP**: ZINC dataset, but the model was optimized with Proximal Policy Optimization (PPO) to generate molecules with high pLogP scores.
+
+**Model type**: A flow-based autoregressive graph molecular generative model that can be optimized with RL for goal-directed graph generation.
+
+**Information about training algorithms, parameters, fairness constraints or other applied approaches, and features**: Default parameters as provided in [(TorchDrug tutorial)](https://torchdrug.ai/docs/tutorials/generation.html).
+
+**Paper or other resource for more information**: [GraphAF: a flow-based autoregressive model for molecular graph generation (*ICLR 2020*)](https://openreview.net/pdf?id=S1esMkHYPr).
+
+**License**: TorchDrug: Apache-2.0 license.
+
+**Where to send questions or comments about the model**: Open an issue on [TorchDrug repository](https://github.com/DeepGraphLearning/torchdrug) or ask original authors.
+
+**Intended Use. Use cases that were envisioned during development**: Chemical research, in particular drug discovery.
+
+**Primary intended uses/users**: Researchers and computational chemists using the model for model comparison or research exploration purposes.
+
+**Out-of-scope use cases**: Production-level inference, producing molecules with harmful properties.
+
+**Factors**: Not applicable.
+
+**Metrics**: Validation loss on decoding correct molecules.
+
+**Datasets**: 250,000 drug-like molecules from [ZINC](https://zinc.docking.org) (with a maximum atom number of 38).
+
+**Ethical Considerations**: Unclear, please consult with original authors in case of questions.
+
+**Caveats and Recommendations**: Unclear, please consult with original authors in case of questions.
+
+Model card prototype inspired by [Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)
+
+## Citation
+
+```bib
+@inproceedings{shi2020graphaf,
+  author    = {Chence Shi and Minkai Xu and Zhaocheng Zhu and Weinan Zhang and Ming Zhang and Jian Tang},
+  title     = {GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation},
+  booktitle = {International Conference on Learning Representations, {ICLR} 2020},
+  year      = {2020},
+  url       = {https://openreview.net/forum?id=S1esMkHYPr}
+}
+```
+

model_cards/description.md

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+<img align="right" src="https://raw.githubusercontent.com/GT4SD/gt4sd-core/main/docs/_static/gt4sd_logo.png" alt="logo" width="120" >
+
+
+[TorchDrug](https://github.com/DeepGraphLearning/torchdrug) is a PyTorch toolbox on graph models for drug discovery.
+We, the developers of **GT4SD** (Generative Toolkit for Scientific Discovery), provide access to two graph-based molecular generative models distributed by TorchDrug:
+- **GCPN**: Graph Convolutional Policy Network ([You et al., (2018), *NeurIPS*](https://proceedings.neurips.cc/paper/2018/hash/d60678e8f2ba9c540798ebbde31177e8-Abstract.html))
+- **GraphAF**: GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation ([Shi et al., (2020), *ICLR*](https://openreview.net/forum?id=S1esMkHYPr))
+
+For **examples** and **documentation** of the model parameters, please see below.
+Moreover, we provide a **model card** ([Mitchell et al. (2019)](https://dl.acm.org/doi/abs/10.1145/3287560.3287596?casa_token=XD4eHiE2cRUAAAAA:NL11gMa1hGPOUKTAbtXnbVQBDBbjxwcjGECF_i-WC_3g1aBgU1Hbz_f2b4kI_m1in-w__1ztGeHnwHs)) at the bottom of this page.

model_cards/examples.csv

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+GCPN,zinc250k_v0,5
+GCPN,qed_v0,10
+GraphAF,plogp_v0,5
+

requirements.txt

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+-f https://download.pytorch.org/whl/cpu/torch_stable.html
+-f https://data.pyg.org/whl/torch-1.12.1+cpu.html
+# pip==20.2.4
+torch==1.12.1
+torch-scatter
+torch-spline-conv
+torch-sparse
+torch-geometric
+torchvision==0.13.1
+torchaudio==0.12.1
+gt4sd>=1.0.0
+molgx>=0.22.0a1
+molecule_generation
+nglview
+PyTDC==0.3.7
+gradio==3.12.0
+markdown-it-py>=2.1.0
+mols2grid>=0.2.0
+numpy==1.23.5
+pandas>=1.0.0
+terminator @ git+https://github.com/IBM/regression-transformer@gt4sd
+guacamol_baselines @ git+https://github.com/GT4SD/guacamol_baselines.git@v0.0.2
+moses @ git+https://github.com/GT4SD/moses.git@v0.1.0
+paccmann_chemistry @ git+https://github.com/PaccMann/paccmann_chemistry@0.0.4
+paccmann_generator @ git+https://github.com/PaccMann/paccmann_generator@0.0.2
+paccmann_gp @ git+https://github.com/PaccMann/paccmann_gp@0.1.1
+paccmann_omics @ git+https://github.com/PaccMann/paccmann_omics@0.0.1.1
+paccmann_predictor @ git+https://github.com/PaccMann/paccmann_predictor@sarscov2
+reinvent_models @ git+https://github.com/GT4SD/reinvent_models@v0.0.1

utils.py

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+import logging
+from collections import defaultdict
+from typing import List
+
+import mols2grid
+import pandas as pd
+
+logger = logging.getLogger(__name__)
+logger.addHandler(logging.NullHandler())
+
+
+def draw_grid_generate(
+    samples: List[str],
+    seeds: List[str] = [],
+    n_cols: int = 3,
+    size=(140, 200),
+) -> str:
+    """
+    Uses mols2grid to draw a HTML grid for the generated molecules
+
+    Args:
+        samples: The generated samples.
+        n_cols: Number of columns in grid. Defaults to 5.
+        size: Size of molecule in grid. Defaults to (140, 200).
+
+    Returns:
+        HTML to display
+    """
+
+    result = defaultdict(list)
+    result.update(
+        {
+            "SMILES": seeds + samples,
+            "Name": [f"Seed_{i}" for i in range(len(seeds))]
+            + [f"Generated_{i}" for i in range(len(samples))],
+        },
+    )
+
+    result_df = pd.DataFrame(result)
+    obj = mols2grid.display(
+        result_df,
+        tooltip=list(result.keys()),
+        height=1100,
+        n_cols=n_cols,
+        name="Results",
+        size=size,
+    )
+    return obj.data