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PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding

📢 News

overall_framework

We propose PrefixMol, inserting learnable conditional feature vectors into the attention module to unify multi-conditional molecule generative models to support the modeling of customized requirements.

Installation

Dependency

The codes have been tested in the following environment.

PackageVersion
Python3.7.12
PyTorch1.10.0
CUDA11.3.1
PyTorch Geometric2.0.3
RDKit2021.09.4

Install via conda yaml file (cuda 11.3)

conda env create -f env.yml
conda activate PrefixMol

Install manually

conda create -n PrefixMol python=3.7.12
conda activate PrefixMol
# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg
# Install other tools # conda install
conda install -c conda-forge rdkit
conda install -c conda-forge openbabel
conda install pyyaml easydict python-lmdb -c conda-forge
# pip install
pip install nni
pip install dill
pip install deepchem
pip install jax
pip install jaxlib
pip install tensorflow
pip install transformers
pip install partialsmiles
pip install pytorch-lightning
🔎 Tips for pytorch3d installation Notice that we recommend using the following steps to install pytorch3d 👐
  1. install the following necessary packages.
conda install -c fvcore -c iopath -c conda-forge fvcore iopath
  1. Find the suitable version with your environment
  2. Git clone the resporitory and then run the command as follows for example.
cd pytorch3d
python setup.py install

Datasets

Please refer to README.md in the data folder.

Training

We used DDP to accelerate the training process. Here are some command examples FYR.

# 4 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 train.py
# 8 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node 8 train.py

Testing

When it comes to testing process, we loaded the checkpoint.pth and used 1 GPU to test the result.

CUDA_VISIBLE_DEVICES="0" python -m torch.distributed.launch --nproc_per_node 1 test.py

🔖 Tips

When running the codes, the path where the code appears is recommended to be changed to the path you need at the moment.

Citation

@article{gao2023prefixmol,
title={PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding},
author={Gao, Zhangyang and Hu, Yuqi and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2302.07120},
year={2023}
}

Contact

Zhangyang Gao (gaozhangyang@westlake.edu.cn) Yuqi Hu (hyqale1024@gmail.com)

About

The official implementation of the paper "PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding".

Resources

Readme
Activity
Custom properties

Stars

9 stars

Watchers

0 watching

Forks

2 forks
Report repository

Releases

Packages

Contributors

Languages

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
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Repository files navigation
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
📢 News
overall_framework

We propose PrefixMol, inserting learnable conditional feature vectors into the attention module to unify multi-conditional molecule generative models to support the modeling of customized requirements.

Installation
Dependency
The codes have been tested in the following environment.
PackageVersion
Python3.7.12
PyTorch1.10.0
CUDA11.3.1
PyTorch Geometric2.0.3
RDKit2021.09.4
Install via conda yaml file (cuda 11.3)
conda env create -f env.yml
conda activate PrefixMol
Install manually
conda create -n PrefixMol python=3.7.12
conda activate PrefixMol
# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg
# Install other tools # conda install
conda install -c conda-forge rdkit
conda install -c conda-forge openbabel
conda install pyyaml easydict python-lmdb -c conda-forge
# pip install
pip install nni
pip install dill
pip install deepchem
pip install jax
pip install jaxlib
pip install tensorflow
pip install transformers
pip install partialsmiles
pip install pytorch-lightning
🔎 Tips for pytorch3d installation
Notice that we recommend using the following steps to install pytorch3d 👐 
  1. install the following necessary packages.
conda install -c fvcore -c iopath -c conda-forge fvcore iopath

  1. Find the suitable version with your environment
  2. Git clone the resporitory and then run the command as follows for example.
cd pytorch3d
python setup.py install
Datasets
Please refer to README.md in the data folder.
Training
We used DDP to accelerate the training process. Here are some command examples FYR.
# 4 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 train.py
# 8 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node 8 train.py

Testing
When it comes to testing process, we loaded the checkpoint.pth and used 1 GPU to test the result.
CUDA_VISIBLE_DEVICES="0" python -m torch.distributed.launch --nproc_per_node 1 test.py

🔖 Tips
When running the codes, the path where the code appears is recommended to be changed to the path you need at the moment.
Citation
@article{gao2023prefixmol,
title={PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding},
author={Gao, Zhangyang and Hu, Yuqi and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2302.07120},
year={2023}
}

Contact
Zhangyang Gao (gaozhangyang@westlake.edu.cn)
Yuqi Hu (hyqale1024@gmail.com)
About
The official implementation of the paper "PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding".
Resources
Readme
Activity
Custom properties
Stars
9 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
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})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
📢 News
overall_framework

We propose PrefixMol, inserting learnable conditional feature vectors into the attention module to unify multi-conditional molecule generative models to support the modeling of customized requirements.

Installation
Dependency
The codes have been tested in the following environment.
PackageVersion
Python3.7.12
PyTorch1.10.0
CUDA11.3.1
PyTorch Geometric2.0.3
RDKit2021.09.4
Install via conda yaml file (cuda 11.3)
conda env create -f env.yml
conda activate PrefixMol
Install manually
conda create -n PrefixMol python=3.7.12
conda activate PrefixMol
# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg
# Install other tools # conda install
conda install -c conda-forge rdkit
conda install -c conda-forge openbabel
conda install pyyaml easydict python-lmdb -c conda-forge
# pip install
pip install nni
pip install dill
pip install deepchem
pip install jax
pip install jaxlib
pip install tensorflow
pip install transformers
pip install partialsmiles
pip install pytorch-lightning
🔎 Tips for pytorch3d installation
Notice that we recommend using the following steps to install pytorch3d 👐 
  1. install the following necessary packages.
conda install -c fvcore -c iopath -c conda-forge fvcore iopath

  1. Find the suitable version with your environment
  2. Git clone the resporitory and then run the command as follows for example.
cd pytorch3d
python setup.py install
Datasets
Please refer to README.md in the data folder.
Training
We used DDP to accelerate the training process. Here are some command examples FYR.
# 4 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 train.py
# 8 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node 8 train.py

Testing
When it comes to testing process, we loaded the checkpoint.pth and used 1 GPU to test the result.
CUDA_VISIBLE_DEVICES="0" python -m torch.distributed.launch --nproc_per_node 1 test.py

🔖 Tips
When running the codes, the path where the code appears is recommended to be changed to the path you need at the moment.
Citation
@article{gao2023prefixmol,
title={PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding},
author={Gao, Zhangyang and Hu, Yuqi and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2302.07120},
year={2023}
}

Contact
Zhangyang Gao (gaozhangyang@westlake.edu.cn)
Yuqi Hu (hyqale1024@gmail.com)
About
The official implementation of the paper "PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding".
Resources
Readme
Activity
Custom properties
Stars
9 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
📢 News
overall_framework

We propose PrefixMol, inserting learnable conditional feature vectors into the attention module to unify multi-conditional molecule generative models to support the modeling of customized requirements.

Installation
Dependency
The codes have been tested in the following environment.
PackageVersion
Python3.7.12
PyTorch1.10.0
CUDA11.3.1
PyTorch Geometric2.0.3
RDKit2021.09.4
Install via conda yaml file (cuda 11.3)
conda env create -f env.yml
conda activate PrefixMol
Install manually
conda create -n PrefixMol python=3.7.12
conda activate PrefixMol
# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg
# Install other tools # conda install
conda install -c conda-forge rdkit
conda install -c conda-forge openbabel
conda install pyyaml easydict python-lmdb -c conda-forge
# pip install
pip install nni
pip install dill
pip install deepchem
pip install jax
pip install jaxlib
pip install tensorflow
pip install transformers
pip install partialsmiles
pip install pytorch-lightning
🔎 Tips for pytorch3d installation
Notice that we recommend using the following steps to install pytorch3d 👐 
  1. install the following necessary packages.
conda install -c fvcore -c iopath -c conda-forge fvcore iopath

  1. Find the suitable version with your environment
  2. Git clone the resporitory and then run the command as follows for example.
cd pytorch3d
python setup.py install
Datasets
Please refer to README.md in the data folder.
Training
We used DDP to accelerate the training process. Here are some command examples FYR.
# 4 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 train.py
# 8 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node 8 train.py

Testing
When it comes to testing process, we loaded the checkpoint.pth and used 1 GPU to test the result.
CUDA_VISIBLE_DEVICES="0" python -m torch.distributed.launch --nproc_per_node 1 test.py

🔖 Tips
When running the codes, the path where the code appears is recommended to be changed to the path you need at the moment.
Citation
@article{gao2023prefixmol,
title={PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding},
author={Gao, Zhangyang and Hu, Yuqi and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2302.07120},
year={2023}
}

Contact
Zhangyang Gao (gaozhangyang@westlake.edu.cn)
Yuqi Hu (hyqale1024@gmail.com)
About
The official implementation of the paper "PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding".
Resources
Readme
Activity
Custom properties
Stars
9 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
📢 News
overall_framework

We propose PrefixMol, inserting learnable conditional feature vectors into the attention module to unify multi-conditional molecule generative models to support the modeling of customized requirements.

Installation
Dependency
The codes have been tested in the following environment.
PackageVersion
Python3.7.12
PyTorch1.10.0
CUDA11.3.1
PyTorch Geometric2.0.3
RDKit2021.09.4
Install via conda yaml file (cuda 11.3)
conda env create -f env.yml
conda activate PrefixMol
Install manually
conda create -n PrefixMol python=3.7.12
conda activate PrefixMol
# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg
# Install other tools # conda install
conda install -c conda-forge rdkit
conda install -c conda-forge openbabel
conda install pyyaml easydict python-lmdb -c conda-forge
# pip install
pip install nni
pip install dill
pip install deepchem
pip install jax
pip install jaxlib
pip install tensorflow
pip install transformers
pip install partialsmiles
pip install pytorch-lightning
🔎 Tips for pytorch3d installation
Notice that we recommend using the following steps to install pytorch3d 👐 
  1. install the following necessary packages.
conda install -c fvcore -c iopath -c conda-forge fvcore iopath

  1. Find the suitable version with your environment
  2. Git clone the resporitory and then run the command as follows for example.
cd pytorch3d
python setup.py install
Datasets
Please refer to README.md in the data folder.
Training
We used DDP to accelerate the training process. Here are some command examples FYR.
# 4 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 train.py
# 8 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node 8 train.py

Testing
When it comes to testing process, we loaded the checkpoint.pth and used 1 GPU to test the result.
CUDA_VISIBLE_DEVICES="0" python -m torch.distributed.launch --nproc_per_node 1 test.py

🔖 Tips
When running the codes, the path where the code appears is recommended to be changed to the path you need at the moment.
Citation
@article{gao2023prefixmol,
title={PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding},
author={Gao, Zhangyang and Hu, Yuqi and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2302.07120},
year={2023}
}

Contact
Zhangyang Gao (gaozhangyang@westlake.edu.cn)
Yuqi Hu (hyqale1024@gmail.com)
About
The official implementation of the paper "PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding".
Resources
Readme
Activity
Custom properties
Stars
9 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
📢 News
overall_framework

We propose PrefixMol, inserting learnable conditional feature vectors into the attention module to unify multi-conditional molecule generative models to support the modeling of customized requirements.

Installation
Dependency
The codes have been tested in the following environment.
PackageVersion
Python3.7.12
PyTorch1.10.0
CUDA11.3.1
PyTorch Geometric2.0.3
RDKit2021.09.4
Install via conda yaml file (cuda 11.3)
conda env create -f env.yml
conda activate PrefixMol
Install manually
conda create -n PrefixMol python=3.7.12
conda activate PrefixMol
# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg
# Install other tools # conda install
conda install -c conda-forge rdkit
conda install -c conda-forge openbabel
conda install pyyaml easydict python-lmdb -c conda-forge
# pip install
pip install nni
pip install dill
pip install deepchem
pip install jax
pip install jaxlib
pip install tensorflow
pip install transformers
pip install partialsmiles
pip install pytorch-lightning
🔎 Tips for pytorch3d installation
Notice that we recommend using the following steps to install pytorch3d 👐 
  1. install the following necessary packages.
conda install -c fvcore -c iopath -c conda-forge fvcore iopath

  1. Find the suitable version with your environment
  2. Git clone the resporitory and then run the command as follows for example.
cd pytorch3d
python setup.py install
Datasets
Please refer to README.md in the data folder.
Training
We used DDP to accelerate the training process. Here are some command examples FYR.
# 4 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 train.py
# 8 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node 8 train.py

Testing
When it comes to testing process, we loaded the checkpoint.pth and used 1 GPU to test the result.
CUDA_VISIBLE_DEVICES="0" python -m torch.distributed.launch --nproc_per_node 1 test.py

🔖 Tips
When running the codes, the path where the code appears is recommended to be changed to the path you need at the moment.
Citation
@article{gao2023prefixmol,
title={PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding},
author={Gao, Zhangyang and Hu, Yuqi and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2302.07120},
year={2023}
}

Contact
Zhangyang Gao (gaozhangyang@westlake.edu.cn)
Yuqi Hu (hyqale1024@gmail.com)
About
The official implementation of the paper "PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding".
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, 'i');
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try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
📢 News
overall_framework

We propose PrefixMol, inserting learnable conditional feature vectors into the attention module to unify multi-conditional molecule generative models to support the modeling of customized requirements.

Installation
Dependency
The codes have been tested in the following environment.
PackageVersion
Python3.7.12
PyTorch1.10.0
CUDA11.3.1
PyTorch Geometric2.0.3
RDKit2021.09.4
Install via conda yaml file (cuda 11.3)
conda env create -f env.yml
conda activate PrefixMol
Install manually
conda create -n PrefixMol python=3.7.12
conda activate PrefixMol
# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg
# Install other tools # conda install
conda install -c conda-forge rdkit
conda install -c conda-forge openbabel
conda install pyyaml easydict python-lmdb -c conda-forge
# pip install
pip install nni
pip install dill
pip install deepchem
pip install jax
pip install jaxlib
pip install tensorflow
pip install transformers
pip install partialsmiles
pip install pytorch-lightning
🔎 Tips for pytorch3d installation
Notice that we recommend using the following steps to install pytorch3d 👐 
  1. install the following necessary packages.
conda install -c fvcore -c iopath -c conda-forge fvcore iopath

  1. Find the suitable version with your environment
  2. Git clone the resporitory and then run the command as follows for example.
cd pytorch3d
python setup.py install
Datasets
Please refer to README.md in the data folder.
Training
We used DDP to accelerate the training process. Here are some command examples FYR.
# 4 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 train.py
# 8 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node 8 train.py

Testing
When it comes to testing process, we loaded the checkpoint.pth and used 1 GPU to test the result.
CUDA_VISIBLE_DEVICES="0" python -m torch.distributed.launch --nproc_per_node 1 test.py

🔖 Tips
When running the codes, the path where the code appears is recommended to be changed to the path you need at the moment.
Citation
@article{gao2023prefixmol,
title={PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding},
author={Gao, Zhangyang and Hu, Yuqi and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2302.07120},
year={2023}
}

Contact
Zhangyang Gao (gaozhangyang@westlake.edu.cn)
Yuqi Hu (hyqale1024@gmail.com)
About
The official implementation of the paper "PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding".
Resources
Readme
Activity
Custom properties
Stars
9 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
PrefixMol: Target- and Chemistry-aware Molecule Design via Prefix Embedding
📢 News
overall_framework

We propose PrefixMol, inserting learnable conditional feature vectors into the attention module to unify multi-conditional molecule generative models to support the modeling of customized requirements.

Installation
Dependency
The codes have been tested in the following environment.
PackageVersion
Python3.7.12
PyTorch1.10.0
CUDA11.3.1
PyTorch Geometric2.0.3
RDKit2021.09.4
Install via conda yaml file (cuda 11.3)
conda env create -f env.yml
conda activate PrefixMol
Install manually
conda create -n PrefixMol python=3.7.12
conda activate PrefixMol
# Install PyTorch (for cuda 11.3)
conda install pytorch==1.10.0 cudatoolkit=11.3 -c pytorch -c conda-forge
# Install PyTorch Geometric (>=2.0.0)
conda install pyg -c pyg
# Install other tools # conda install
conda install -c conda-forge rdkit
conda install -c conda-forge openbabel
conda install pyyaml easydict python-lmdb -c conda-forge
# pip install
pip install nni
pip install dill
pip install deepchem
pip install jax
pip install jaxlib
pip install tensorflow
pip install transformers
pip install partialsmiles
pip install pytorch-lightning
🔎 Tips for pytorch3d installation
Notice that we recommend using the following steps to install pytorch3d 👐 
  1. install the following necessary packages.
conda install -c fvcore -c iopath -c conda-forge fvcore iopath

  1. Find the suitable version with your environment
  2. Git clone the resporitory and then run the command as follows for example.
cd pytorch3d
python setup.py install
Datasets
Please refer to README.md in the data folder.
Training
We used DDP to accelerate the training process. Here are some command examples FYR.
# 4 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3" python -m torch.distributed.launch --nproc_per_node 4 train.py
# 8 GPUs
CUDA_VISIBLE_DEVICES="0,1,2,3,4,5,6,7" python -m torch.distributed.launch --nproc_per_node 8 train.py

Testing
When it comes to testing process, we loaded the checkpoint.pth and used 1 GPU to test the result.
CUDA_VISIBLE_DEVICES="0" python -m torch.distributed.launch --nproc_per_node 1 test.py

🔖 Tips
When running the codes, the path where the code appears is recommended to be changed to the path you need at the moment.
Citation
@article{gao2023prefixmol,
title={PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding},
author={Gao, Zhangyang and Hu, Yuqi and Tan, Cheng and Li, Stan Z},
journal={arXiv preprint arXiv:2302.07120},
year={2023}
}

Contact
Zhangyang Gao (gaozhangyang@westlake.edu.cn)
Yuqi Hu (hyqale1024@gmail.com)
About
The official implementation of the paper "PrefixMol: Target-and Chemistry-aware Molecule Design via Prefix Embedding".
Resources
Readme
Activity
Custom properties
Stars
9 stars
Watchers
0 watching
Forks
2 forks
Report repository
Releases
Packages
Contributors
Languages