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Merge pull request #35 from GreatRSingh/main
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Information Pages
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@rbharath
rbharath authored Jan 30, 2024
2 parents 40d5f81 + 6b2ac4c commit d6d66bd
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21 changes: 21 additions & 0 deletions new-website/utils/tutorials/acknowledgement.html
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<html>
<style>
h1 {
margin-top: 250px;
padding-left: 80px;
padding-right: 80px;
font-size: 50px;
}
.general {
padding-left: 80px;
padding-right: 80px;
font-size: 20px;
}
</style>
<body>
<h1>Acknowledgement</h1>
<p class='general'>
We acknowledge the DeepChem community for their contributions and support.
</p>
</body>
</html>
2 changes: 2 additions & 0 deletions new-website/utils/tutorials/acknowledgement.md
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# Acknowledgement
We acknowledge the DeepChem community for their contributions and support.
16 changes: 14 additions & 2 deletions new-website/utils/tutorials/build_pdf_book.py
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Requirements:
- pdfunite
- pdfkit
- mdpdf
Example Usage:
- Run the script "fetch_tutorials.py" // It will fetch all the tutorials.
Expand Down Expand Up @@ -72,10 +73,21 @@ def merge_pdf_pages(a: List[str]):
command = "pdfunite "
for i in a:
command = command + i + ' '
return command
#os.system(command, "merged.pdf")
os.system(command + "storage/merged.pdf")

def compile_information_pages():
"""Converts the Acknowledgent page and content page from
Html to pdf, then they can be merged with the content
pdf using `merge_pdf_pages` function.
"""
pdfkit.from_file('title.html', 'storage/title.pdf')
pdfkit.from_file('contents.html', 'storage/contents.pdf')
pdfkit.from_file('acknowledgement.html', 'storage/acknowledgement.pdf')

if __name__ == "__main__":
os.system("mkdir " + PDF_PATH)
html_to_pdf()
merge_pdf()
compile_information_pages()
merge_pdf_pages(['storage/title.pdf', 'storage/acknowledgement.pdf', 'storage/contents.pdf', 'storage/full_pdf.pdf'])
125 changes: 125 additions & 0 deletions new-website/utils/tutorials/contents.html
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<html>
<style>
h1 {
margin-top: 50px;
font-size: 50px;
}
ul {
padding-left: 40px;
padding-right: 40px;
font-size: 20px;
}
</style>
<body>
<center><h1>Contents</h1></center>
<ul>
<li>
<h2>1. Introduction To Deepchem</h2>
<ol>
<li>The Basic Tools of the Deep Life Sciences</li>
<li>Working With Datasets</li>
<li>An Introduction To MoleculeNet</li>
<li>Molecular Fingerprints</li>
<li>Creating Models with TensorFlow and PyTorch</li>
<li>Introduction to Graph Convolutions</li>
<li>Going Deeper on Molecular Featurizations</li>
<li>Working With Splitters</li>
<li>Advanced Model Training</li>
<li>Creating a high fidelity model from experimental data</li>
<li>Putting Multitask Learning to Work</li>
<li>Modeling Protein Ligand Interactions</li>
<li>Modeling Protein Ligand Interactions With Atomic Convolutions</li>
<li>Conditional Generative Adversarial Networks</li>
<li>Training a Generative Adversarial Network on MNIST</li>
<li>Advanced model training using hyperopt</li>
<li>Introduction to Gaussian Processes</li>
<li>PytorchLightning Integration</li>
</ol>
</li>
<li>
<h2>2. Molecular Machine Learning</h2>
<ol>
<li>Molecular Fingerprints
<li>Going Deeper on Molecular Featurizations
<li>Learning Unsupervised Embeddings for Molecules
<li>Atomic Contributions for Molecules
<li>Interactive Model Evaluation with Trident Chemwidgets
<li>Transfer Learning With ChemBERTa Transformers
<li>Training a Normalizing Flow on QM9
<li>Large Scale Chemical Screens
<li>Introduction to Molecular Attention Transformer
<li>Generating molecules with MolGAN
<li>Introduction to GROVER
</ol>
</li>
<li>
<h2>3. Modeling Proteins</h2>
<ol>
<li>Protein Deep Learning
</ol>
</li>
<li>
<h2>4. Protein Ligand Modeling</h2>
<ol>
<li>Modeling Protein Ligand Interactions
<li>Modeling Protein Ligand Interactions With Atomic Convolutions
<li>DeepChemXAlphafold
</ol>
</li>
<li>
<h2>5. Quantum Chemistry</h2>
<ol>
<li>Exploring Quantum Chemistry with GDB1k
<li>DeepQMC tutorial
<li>Training an Exchange Correlation Functional using Deepchem
</ol>
</li>
<li>
<h2>6. Bioinformatics</h2>
<ol>
<li>Introduction to Bioinformatics
<li>Multisequence Alignments
<li>Deep probabilistic analysis of single-cell omics data
</ol>
</li>
<li>
<h2>7. Material Sciences</h2>
<ol>
<li>Introduction To Material Science
</ol>
</li>
<li>
<h2>8. Machine Learning Methods</h2>
<ol>
<li>Using Reinforcement Learning to Play Pong
<li>Introduction to Model Interpretability
<li>Uncertainty In Deep Learning
</ol>
</li>
<li>
<h2>9. Deep Differential Equations</h2>
<ol>
<li>Physics Informed Neural Networks
<li>Introducing JaxModel and PINNModel
<li>About Neural ODE : Using Torchdiffeq with Deepchem
</ol>
</li>
<li>
<h2>10. Equivariance</h2>
<ol>
<li>Introduction to Equivariance
<li>Modeling Protein Ligand Interactions With Atomic Convolutions
<li>DeepChemXAlphafold
</ol>
</li>
<li>
<h2>11. Olfaction</h2>
<ol>
<li>Predict Multi Label Odor Descriptors using OpenPOM
</ol>
</li>
</ul>

</body>

</html>
71 changes: 71 additions & 0 deletions new-website/utils/tutorials/contents.md
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# Contents

### 1. Introduction To Deepchem
1. The Basic Tools of the Deep Life Sciences
2. Working With Datasets
3. An Introduction To MoleculeNet
4. Molecular Fingerprints
5. Creating Models with TensorFlow and PyTorch
6. Introduction to Graph Convolutions
7. Going Deeper on Molecular Featurizations
8. Working With Splitters
9. Advanced Model Training
10. Creating a high fidelity model from experimental data
11. Putting Multitask Learning to Work
12. Modeling Protein Ligand Interactions
13. Modeling Protein Ligand Interactions With Atomic Convolutions
14. Conditional Generative Adversarial Networks
15. Training a Generative Adversarial Network on MNIST
16. Advanced model training using hyperopt
17. Introduction to Gaussian Processes
18. PytorchLightning Integration

### 2. Molecular Machine Learning
1. Molecular Fingerprints
2. Going Deeper on Molecular Featurizations
3. Learning Unsupervised Embeddings for Molecules
4. Atomic Contributions for Molecules
5. Interactive Model Evaluation with Trident Chemwidgets
6. Transfer Learning With ChemBERTa Transformers
7. Training a Normalizing Flow on QM9
8. Large Scale Chemical Screens
9. Introduction to Molecular Attention Transformer
10. Generating molecules with MolGAN
11. Introduction to GROVER

### 3. Modeling Proteins
1. Protein Deep Learning

### 4. Protein Ligand Modeling
1. Modeling Protein Ligand Interactions
2. Modeling Protein Ligand Interactions With Atomic Convolutions
3. DeepChemXAlphafold

### 5. Quantum Chemistry
1. Exploring Quantum Chemistry with GDB1k
2. DeepQMC tutorial
3. Training an Exchange Correlation Functional using Deepchem

### 6. Bioinformatics
1. Introduction to Bioinformatics
2. Multisequence Alignments
3. Deep probabilistic analysis of single-cell omics data

### 7. Material Sciences
1. Introduction To Material Science

### 8. Machine Learning Methods
1. Using Reinforcement Learning to Play Pong
2. Introduction to Model Interpretability
3. Uncertainty In Deep Learning

### 9. Deep Differential Equations
1. Physics Informed Neural Networks
2. Introducing JaxModel and PINNModel
3. About Neural ODE : Using Torchdiffeq with Deepchem

### 10. Equivariance
1. Introduction to Equivariance

### 11. Olfaction
1. Predict Multi Label Odor Descriptors using OpenPOM
30 changes: 30 additions & 0 deletions new-website/utils/tutorials/title.html
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<html>
<style>
h1 {
margin-top: 250px;
padding-left: 80px;
padding-right: 80px;
font-size: 50px;
}
.author {
margin-top: 200px;
padding-left: 80px;
padding-right: 80px;
font-size: 20px;
}
.general {
padding-left: 80px;
padding-right: 80px;
font-size: 20px;
}
</style>
<body>
<h1>The DeepChem Book</h1>
<p class='general'>
Democratizing Deep-Learning for Drug Discovery Quantum Chemistry, Materials Science and Biology
</p>
<p class='author'>
Bharath Ramsundar
</p>
</body>
</html>

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