deepchem.github.io/new-website/utils/tutorials/contents.html at 5b1fa8030ac063f148e609b8c0a02f78e7ffd3b8 · deepchem/deepchem.github.io · GitHub

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  <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: Representing Molecules for Deep-Learning</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>Pytorch Lightning Integration for DeepChem Models</li>
          </ol>
      </li>
      <li>
          <h2>2. More Molecular Machine Learning</h2>
          <ol>
              <li>Going Deeper on Molecular Featurizations</li>
              <li>Learning Unsupervised Embeddings for Molecules</li>
              <li>Atomic Contributions for Molecules</li>
              <li>Interactive Model Evaluation with Trident Chemwidgets</li>
              <li>Transfer Learning With ChemBERTa Transformers</li>
              <li>Training a Normalizing Flow on QM9</li>
              <li>Large Scale Chemical Screens</li>
              <li>Introduction to Molecular Attention Transformer</li>
              <li>Generating molecules with MolGAN</li>
              <li>Introduction to GROVER</li>
          </ol>
      </li>
      <li>
          <h2>3. Modeling Proteins</h2>
          <ol>
              <li>Protein Deep Learning</li>
          </ol>
      </li>
      <li>
          <h2>4. Protein Ligand Modeling</h2>
          <ol>
              <li>Modeling Protein Ligand Interactions</li>
              <li>Applications of DeepChem with Alphafold: Docking and protein-ligand interaction from protein sequence</li>
          </ol>
      </li>
      <li>
          <h2>5. Quantum Chemistry</h2>
          <ol>
              <li>Exploring Quantum Chemistry with GDB1k</li>
              <li>DeepQMC integration with DeepChem tutorial</li>
              <li>Training an Exchange Correlation Functional using Deepchem</li>
          </ol>
      </li>
      <li>
          <h2>6. Bioinformatics</h2>
          <ol>
              <li>Introduction to Bioinformatics</li>
              <li>Multisequence Alignments</li>
              <li>Deep probabilistic analysis of single-cell omics data</li>
          </ol>
      </li>
      <li>
          <h2>7. Material Sciences</h2>
          <ol>
              <li>Introduction To Material Science</li>
          </ol>
      </li>
      <li>
          <h2>8. Machine Learning Methods</h2>
          <ol>
              <li>Using Reinforcement Learning to Play Pong</li>
              <li>Introduction to Model Interpretability</li>
              <li>Uncertainty In Deep Learning</li>
          </ol>
      </li>
      <li>
          <h2>9. Deep Differential Equations</h2>
          <ol>
              <li>Physics Informed Neural Networks</li>
              <li>Introducing JaxModel and PINNModel</li>
              <li>About Neural ODE : Using Torchdiffeq with Deepchem</li>
          </ol>
      </li>
      <li>
          <h2>10. Equivariance</h2>
          <ol>
              <li>Introduction to Equivariance</li>
          </ol>
      </li>
      <li>
          <h2>11. Olfaction</h2>
          <ol>
              <li>Predict Multi Label Odor Descriptors using OpenPOM</li>
          </ol>
      </li>
  </ul>

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