reordering tutorials by category [skip ci]

# DeepChem Step-by-Step Tutorial

In this tutorial series, you'll learn how to use DeepChem to solve interesting

and challenging problems in the life sciences. This tutorial series is

and challenging problems in the life sciences. The tutorial acts as a introduction

to DeepChem as well as application of DeepChem to a wide array of problems

across domains like molecular machine learning,

quantum chemistry, bioinformatics and material science. This tutorial series is

continually updated with new DeepChem features and models as implemented and is

designed to be accessible to beginners.

you can help democratize these skills and open up drug discovery to more

competition. Increased competition can help drive down the cost of medicine.

## You Will Learn

* [Part 1: The Basic Tools of the Deep Life Sciences](01_The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb)

* [Part 2: Learning MNIST Digit Classifiers](02_Learning_MNIST_Digit_Classifiers.ipynb)

* [Part 3: Modeling Solubility](03_Modeling_Solubility.ipynb)

* [Part 4: Introduction to Graph Convolutions](04_Introduction_to_Graph_Convolutions.ipynb)

* [Part 5: Putting Multitask Learning to Work](05_Putting_Multitask_Learning_to_Work.ipynb)

* [Part 6: Going Deeper on Molecular Featurizations](06_Going_Deeper_on_Molecular_Featurizations.ipynb)

* [Part 7: Uncertainty in Deep Learning](07_Uncertainty_In_Deep_Learning.ipynb)

* [Part 8: Introduction to Model Interpretability](08_Introduction_to_Model_Interpretability.ipynb)

* [Part 9: Creating a high fidelity model from experimental data](09_Creating_a_high_fidelity_model_from_experimental_data.ipynb)

* [Part 10: Exploring Quantum Chemistry with GDB1k](10_Exploring_Quantum_Chemistry_with_GDB1k.ipynb)

* [Part 11: Learning Unsupervised Embeddings for Molecules](11_Learning_Unsupervised_Embeddings_for_Molecules.ipynb)

* [Part 12: Predicting Ki of Ligands to a Protein](12_Predicting_Ki_of_Ligands_to_a_Protein.ipynb)

* [Part 13: Modeling Protein Ligand Interactions](13_Modeling_Protein_Ligand_Interactions.ipynb)

* [Part 14: Modeling Protein Ligand Interactions With Atomic Convolutions](14_Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions.ipynb)

* [Part 15: Synthetic Feasibility Scoring](15_Synthetic_Feasibility_Scoring.ipynb)

* [Part 16: Conditional Generative Adversarial Networks](16_Conditional_Generative_Adversarial_Networks.ipynb)

* [Part 17: Training a Generative Adversarial Network on MNIST](17_Training_a_Generative_Adversarial_Network_on_MNIST.ipynb)

* [Part 18: Using Reinforcement Learning to Play Pong](18_Using_Reinforcement_Learning_to_Play_Pong.ipynb)

* [Part 19: Large Scale Chemical Screens](19_Large_Scale_Chemical_Screens.ipynb)

* [Part 20: ConvertingDeepChem Models to TensorFlow Estimators](20_Converting_DeepChem_Models_to_TensorFlow_Estimators.ipynb)

* [Part 21: Introduction to Bioinformatics](21_Introduction_to_Bioinformatics.ipynb)

* [Part 22: Using HuggingFace + Transfer Learning for Toxicity Predictions](22_Transfer_Learning_With_HuggingFace_tox21.ipynb)

The tutorial is organized as follows:

### Introduction to DeepChem

The following tutorials covers the core features of DeepChem. Covering these

tutorials will help you in getting started with DeepChem for machine learning. The later

tutorials discuss about using DeepChem for specific applications.

* [1.1 The Basic Tools of the Deep Life Sciences](01_The_Basic_Tools_of_the_Deep_Life_Sciences.ipynb)

* [1.2 Working With Datasets](02_Working_With_Datasets.ipynb)

* [1.3 An Introduction to MoleculeNet](03_An_Introduction_To_MoleculeNet.ipynb)

* [1.4 Working With Splitters](08_Working_With_Splitters.ipynb)

* [1.5 Creating Models with Tensorflow and Pytorch](05_Creating_Models_with_TensorFlow_and_PyTorch.ipynb)

* [1.6 Advanced Model Training](09_Advanced_Model_Training.ipynb)

* [1.7 Putting Multitask Learning to Work](11_Putting_Multitask_Learning_to_Work.ipynb)

* [1.8 Creating a high fidelity model from experimental data](10_Creating_a_high_fidelity_model_from_experimental_data.ipynb)

* [1.9 Using Reinforcement Learning to Play Pong](26_Using_Reinforcement_Learning_to_Play_Pong.ipynb)

* [1.10 Training a Generative Adversarial Network on MNIST](15_Training_a_Generative_Adversarial_Network_on_MNIST.ipynb)

* [1.11 Conditional Generative Adversarial Networks](14_Conditional_Generative_Adversarial_Networks.ipynb)

* [1.12 Introduction to Graph Convolutions](06_Introduction_to_Graph_Convolutions.ipynb)

* [1.13 Uncertainty in Deep Learning](25_Uncertainty_In_Deep_Learning.ipynb)

* [1.14 Introduction to Model Interpretability](23_Introduction_to_Model_Interpretability.ipynb)

* [1.15 Large Scale Chemical Screens](19_Large_Scale_Chemical_Screens.ipynb)

### Molecular Machine Learning

* [2.1 Molecular Fingerprints](04_Molecular_Fingerprints.ipynb)

* [2.2 Going Deeper on Molecular Featurizations](07_Going_Deeper_on_Molecular_Featurizations.ipynb)

* [2.3 Learning Unsupervised Embeddings for Molecules](16_Learning_Unsupervised_Embeddings_for_Molecules.ipynb)

* [2.4 Synthetic Feasibility Scoring](24_Synthetic_Feasibility_Scoring.ipynb)

* [2.5 Modeling Protein Ligand Interactions](13_Modeling_Protein_Ligand_Interactions.ipynb)

* [2.6 Modeling Protein Ligand Interactions with Atomic Convolutions](14_Modeling_Protein_Ligand_Interactions_With_Atomic_Convolutions.ipynb)

* [2.7 Atomic Contributions for Molecules](28_Atomic_Contributions_for_Molecules.ipynb)

* [2.8 Transfer Learning with ChemBERTa Transformers](22_Transfer_Learning_With_ChemBERTa_Transformers.ipynb)

* [2.9 Protein Deep Learning](30_Protein_Deep_Learning.ipynb)

* [2.10 Training a Normalizing Flow on QM9](Training_a_Normalizing_Flow_on_QM9.ipynb)

### Quantum Chemistry

* [3.1 Exploring Quantum Chemistry with GDB1k](21_Exploring_Quantum_Chemistry_with_GDB1k.ipynb)

### Bioinformatics

* [4.1 Introduction to BioInformatics](21_Introduction_to_Bioinformatics.ipynb)

### Material Science

* [5.1 Introduction to Material Science](31_Introduction_To_Material_Science.ipynb)