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Deep Chem Bharath Ramsundar Democratizing Deep Learning for Sciences www. deepchem. io www. deepforestsci. com Bharath Ramsundar and the Deep Chem Team The Deep Chem Book The Deep Chem Book The Deep Chem Book is a step-by-step tutorial series for deep life sciences. The author, Bharath Ramsundar and the Deep Chem team,... | deepchem.pdf |
The Deep Chem Book Democratizing Deep-Learning for Drug Discovery Quantum Chemistry, Materials Science and Biology Bharath Ramsundar and the Deep Chem Community | deepchem.pdf |
Acknowledgement We acknowledge the Deep Chem community for their contributions and support. | deepchem.pdf |
Contents 1. Introduction To Deepchem 1. The Basic Tools of the Deep Life Sciences 2. Working With Datasets 3. An Introduction To Molecule Net 4. Molecular Fingerprints 5. Creating Models with Tensor Flow and Py Torch 6. Introduction to Graph Convolutions 7. Going Deeper on Molecular Featurizations 8. W... | deepchem.pdf |
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 Interpretabil... | deepchem.pdf |
The Basic Tools of the Deep Life Sciences Welcome to Deep Chem's introductory tutorial for the deep life sciences. This series of notebooks is a step-by-step guide for you to get to know the new tools and techniques needed to do deep learning for the life sciences. We'll start from the basics, assuming that you're ne... | deepchem.pdf |
'2. 5. 0. dev' Training a Model with Deep Chem: A First Example Deep learning can be used to solve many sorts of problems, but the basic workflow is usually the same. Here are the typical steps you follow. 1. Select the data set you will train your model on (or create a new data set if there isn't an existing suita... | deepchem.pdf |
as well. solubilities = model. predict_on_batch ( test_dataset. X [: 10 ]) for molecule, solubility, test_solubility in zip ( test_dataset. ids, solubilities, test_dataset. y ): print ( solubility, test_solubility, molecule ) [-1. 8629359] [-1. 60114461] c1cc2ccc3cccc4ccc(c1)c2c34 [0. 6617248... | deepchem.pdf |
Working With Datasets Data is central to machine learning. This tutorial introduces the Dataset class that Deep Chem uses to store and manage data. It provides simple but powerful tools for efficiently working with large amounts of data. It also is designed to easily interact with other popular Python framework... | deepchem.pdf |
different experiments on that molecule. This dataset has only a single task: "measured log solubility in mols per litre". Also notice that y and w each have shape (113, 1). The second dimension of these arrays usually matches the number of tasks. Accessing Data from a Dataset There are many ways to access the d... | deepchem.pdf |
[-0. 7171799041956278], [-0. 8165893796145915], [-0. 13019062076936566], [-0. 24380144981960986], [-0. 14912575894440638], [0. 9538460397517154], [-0. 07811899078800374], [-0. 18226225075072758], [0. 2532459272752089], [0. 6887541053011454], [... | deepchem.pdf |
[-2. 59649237] Clc1cc(Cl)c(cc1Cl)c2cc(Cl)c(Cl)cc2Cl [1. 74438806] NC(=O)c1cccnc1 [0. 45206488] Sc1ccccc1 [0. 23383741] CNC(=O)Oc1cc(C)cc(C)c1 [-1. 791749] Cl C1CC2C(C1Cl)C3(Cl)C(=C(Cl)C2(Cl)C3(Cl)Cl)Cl [0. 77396223] CSSC [1. 00118389] NC(=O)c1ccccc1 [-0. 05445007] Clc1ccccc1Br [1. 10438039] COC(=O)c1ccccc1OC2OC(COC3O... | deepchem.pdf |
[-0. 0075856] CCSCCSP(=S)(OC)OC [-0. 04971628] CCC(C)C [-0. 68499017] COP(=O)(OC)OC(=CCl)c1cc(Cl)c(Cl)cc1Cl Most deep learning models can process a batch of multiple samples all at once. You can use iterbatches() to iterate over batches of samples. for X, y, w, ids in test_dataset. iterbatches ( batch_s... | deepchem.pdf |
X1 X2 X3 X4 X5 y1 y2 w ids 0 0. 547330 0. 919941 0. 289138 0. 431806 0. 776672 0. 532579 0. 443258 1. 0 0 1 0. 980867 0. 642487 0. 460640 0. 500153 0. 014848 0. 678259 0. 274029 1. 0 1 2 0. 953254 0. 704446 0. 857458 0. 378372 0. 705789 0. 704786 0. 901080 1. 0 2 3 0. 904970 0. 729710 0. 304247 0. 861546 0. 917029 0. 1... | deepchem.pdf |
An Introduction To Molecule Net By Bharath Ramsundar | Twitter One of the most powerful features of Deep Chem is that it comes "batteries included" with datasets to use. The Deep Chem developer community maintains the Molecule Net [1] suite of datasets which maintains a large collection of different scientific data... | deepchem.pdf |
['load_bace_classification', 'load_bace_regression', 'load_bandgap', 'load_bbbc001', 'load_bbbc002', 'load_bbbp', 'load_cell_counting', 'load_chembl', 'load_chembl25', 'load_clearance', 'load_clintox', 'load_delaney', 'load_factors', 'load_function', 'load_hiv', 'load_hopv', 'load_hppb', 'load_kaggle',... | deepchem.pdf |
molecules. dc. molnet. load_delaney : V1. This dataset is also referred to as ESOL in the original paper. dc. molnet. load_sampl : V1. This dataset is also referred to as Free Solv in the original paper. dc. molnet. load_lipo : V1. This dataset is also referred to as Lipophilicity in the original paper. dc. molne... | deepchem.pdf |
dc. molnet. load_bbbc001 : V2 dc. molnet. load_bbbc002 : V2 dc. molnet. load_cell_counting : V2 Materials Properties Datasets These datasets compute properties of various materials. dc. molnet. load_bandgap : V2 dc. molnet. load_perovskite : V2 dc. molnet. load_mp_formation_energy : V2 dc. molnet. load_mp_metallicity :... | deepchem.pdf |
test <Disk Dataset X. shape: (113,), y. shape: (113, 1), w. shape: (113, 1), ids: ['CCCCc1c(C)nc(nc1O)N(C)C ' 'Cc3cc2nc1c(=O)[n H]c(=O)nc1n(CC(O)C(O)C(O)CO)c2cc3C' 'CSc1nc(NC(C)C)nc(NC(C)C)n1' ... 'O=c1[n H]cnc2[n H]ncc12 ' 'CC(=C)C1CC=C(C)C(=O)C1' 'OC(C(=O)c1ccccc1)c2ccccc2'], task_names: ['measured log solubility... | deepchem.pdf |
The Deep Chem Discord hosts a number of scientists, developers, and enthusiasts interested in deep learning for the life sciences. Join the conversation! | deepchem.pdf |
Molecular Fingerprints Molecules can be represented in many ways. This tutorial introduces a type of representation called a "molecular fingerprint". It is a very simple representation that often works well for small drug-like molecules. Colab This tutorial and the rest in this sequence can be done in Google colab.... | deepchem.pdf |
array([[1. 0433141624730409, 1. 0369942196531792, 8. 53921568627451, ..., 1. 060388945752303, 1. 1895710249165168, 1. 0700990099009902], [1. 0433141624730409, 1. 0369942196531792, 1. 1326397919375812, ..., 0. 0, 1. 1895710249165168, 1. 0700990099009902], [0. 0, 0. 0, 0. 0, ..., 1. 06038894... | deepchem.pdf |
Citing This Tutorial If you found this tutorial useful please consider citing it using the provided Bib Te X. @manual { Intro4, title = { Molecular Fingerprints }, organization = { Deep Chem }, author = { Ramsundar, Bharath }, howpublished = { \ url { https : // github. com / deepchem / deepchem /... | deepchem.pdf |
Creating Models with Tensor Flow and Py Torch In the tutorials so far, we have used standard models provided by Deep Chem. This is fine for many applications, but sooner or later you will want to create an entirely new model with an architecture you define yourself. Deep Chem provides integration with both Tensor F... | deepchem.pdf |
torch. nn. Dropout ( 0. 5 ), torch. nn. Linear ( 1000, 1 ) ) model = dc. models. Torch Model ( pytorch_model, dc. models. losses. L2Loss ()) model. fit ( train_dataset, nb_epoch = 50 ) print ( 'training set score:', model. evaluate ( train_dataset, [ metric ])) print ( 'test set score:', model... | deepchem.pdf |
torch_model = Classification Model () output_types = [ 'prediction', 'loss' ] model = dc. models. Torch Model ( torch_model, dc. models. losses. Sigmoid Cross Entropy (), output_types = output_types ) We will use the same BACE dataset. As before, the model will try to do a binary classification task ... | deepchem.pdf |
Introduction to Graph Convolutions In this tutorial we will learn more about "graph convolutions. " These are one of the most powerful deep learning tools for working with molecular data. The reason for this is that molecules can be naturally viewed as graphs. Note how standard chemical diagrams of the sort we're used... | deepchem.pdf |
tasks, datasets, transformers = dc. molnet. load_tox21 ( featurizer = 'Graph Conv' ) train_dataset, valid_dataset, test_dataset = datasets Let's now train a graph convolutional network on this dataset. Deep Chem has the class Graph Conv Model that wraps a standard graph convolutional architecture und... | deepchem.pdf |
gc2_output = self. gc2 ([ gp1_output ] + inputs [ 1 :]) batch_norm2_output = self. batch_norm1 ( gc2_output ) gp2_output = self. gp2 ([ batch_norm2_output ] + inputs [ 1 :]) dense1_output = self. dense1 ( gp2_output ) batch_norm3_output = self. batch_norm3 ( dense1_o... | deepchem.pdf |
Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following wa... | deepchem.pdf |
Going Deeper On Molecular Featurizations One of the most important steps of doing machine learning on molecular data is transforming the data into a form amenable to the application of learning algorithms. This process is broadly called "featurization" and involves turning a molecule into a vector or tensor of some so... | deepchem.pdf |
Max EState Index 2. 125 Min EState Index 1. 25 Max Abs EState Index 2. 125 Min Abs EState Index 1. 25 qed 0. 3854706587740357 Mol Wt 44. 097 Heavy Atom Mol Wt 36. 033 Exact Mol Wt 44. 062600255999996 Num Valence Electrons 20. 0 Num Radical Electrons 0. 0 Of course, there are many more descriptors than this. print ( 'Th... | deepchem.pdf |
Number of available conformers for butane: 3 Now we can create a Coulomb matrix for our molecule. coulomb_mat = dc. feat. Coulomb Matrix ( max_atoms = 20 ) features = coulomb_mat ( propane_mol ) print ( features ) | deepchem.pdf |
[[[36. 8581052 12. 48684429 7. 5619687 2. 85945193 2. 85804514 2. 85804556 1. 4674015 1. 46740144 0. 91279491 1. 14239698 1. 14239675 0. 0. 0. 0. 0. 0. 0. 0. 0. ] [12. 48684429 36. 8581052 12. 48684388 1. 46551218 ... | deepchem.pdf |
Notice that many elements are 0. To combine multiple molecules in a batch we need all the Coulomb matrices to be the same size, even if the molecules have different numbers of atoms. We specified max_atoms=20, so the returned matrix has size (20, 20). The molecule only has 11 atoms, so only an 11 by 11 submatrix... | deepchem.pdf |
Working With Splitters When using machine learning, you typically divide your data into training, validation, and test sets. The Molecule Net loaders do this automatically. But how should you divide up the data? This question seems simple at first, but it turns out to be quite complicated. There are many ways of... | deepchem.pdf |
Random Stratified Splitter Some datasets are very unbalanced: only a tiny fraction of all samples are positive. In that case, random splitting may sometimes lead to the validation or test set having few or even no positive samples for some tasks. That makes it unable to evaluate performance. Random Stratified Split... | deepchem.pdf |
import deepchem as dc splitters = [ 'random', 'scaffold', 'butina', 'fingerprint' ] metric = dc. metrics. Metric ( dc. metrics. roc_auc_score ) for splitter in splitters : tasks, datasets, transformers = dc. molnet. load_tox21 ( featurizer = 'ECFP', splitter = splitter ) tr... | deepchem.pdf |
Advanced Model Training In the tutorials so far we have followed a simple procedure for training models: load a dataset, create a model, call fit(), evaluate it, and call ourselves done. That's fine for an example, but in real machine learning projects the process is usually more complicated. In this tutorial we w... | deepchem.pdf |
{'_dropouts_0. 200000_layer_sizes[500]_learning_rate_0. 001000_n_features_1024_n_tasks_1': 0. 759624393738977, '_dropouts_0. 200000_layer_sizes[500]_learning_rate_0. 000100_n_features_1024_n_tasks_1': 0. 7680791323731138, '_dropouts_0. 500000_layer_sizes[500]_learning_rate_0. 001000_n_features_1024_n_tasks_1': 0. 762... | deepchem.pdf |
model = dc. models. Multitask Classifier ( n_tasks = len ( tasks ), n_features = 1024, layer_sizes = [ 1000 ], dropouts = 0. 2, learning_rate = learning_rate ) ... | deepchem.pdf |
Creating a High Fidelity Dataset from Experimental Data In this tutorial, we will look at what is involved in creating a new Dataset from experimental data. As we will see, the mechanics of creating the Dataset object is only a small part of the process. Most real datasets need significant cleanup and QA before the... | deepchem.pdf |
Parsing data In order to read in the data, we will use the pandas data analysis library. In order to convert the drug names into smiles strings, we will use pubchempy. This isn't a standard Deep Chem dependency, but you can install this library with conda install pubchempy. ! conda install pubchempy import os impor... | deepchem.pdf |
Unnamed: 0 Unnamed: 1 Unnamed: 2 Metric #1 (-120 m V Peak) Unnamed: 4 Unnamed: 5 Unnamed: 6 Unnamed: 7 0 Na N Na N Na N Vehicle Na N 4 Replications Na N 1 TA ## Position TA ID Mean SD Threshold (%) = Mean + 4x SD N #1 (%) N #2 (%) 2 1 1-A02 Penicillin V Potassium-12. 8689 6. 74705 14. 1193-10. 404-18. 1929 3 2 1-A03 My... | deepchem.pdf |
} ion_keys = [ 'H20', 'HBr', 'HCl', '2Br', '2H2O', 'Br', 'Na' ] def compound_to_smiles ( cmpd ): # remove spaces and irregular characters compound = re. sub ( r '([^\s\w]|_)+', '', cmpd ) # replace ion names if needed for ion in ion_keys : ... | deepchem.pdf |
Will need to look more closely at the dataset* Nothing on this particular protein *This will involve plotting, so we will import matplotlib and seaborn. We will also need to look at molecular structures, so we will import rdkit. We will also use the seaborn library which you can install with conda install seaborn. ... | deepchem.pdf |
nan_rows = smiles_data [ smiles_data. isnull (). T. any (). T ] nan_rows [[ 'n1', 'n2' ]] n1 n2 62 Na N-7. 8266 162-12. 8456-11. 4627 175 Na N-6. 61225 187 Na N-8. 23326 233-8. 21781 Na N 262 Na N-12. 8788 288 Na N-2. 34264 300 Na N-8. 19936 301 Na N-10. 4633 303-5. 61374 8. 42267 311 Na N-8. 78722 I don't trust ... | deepchem.pdf |
This looks pretty gaussian, let's get the 95% confidence interval by fitting a gaussian via scipy, and taking 2*the standard deviation from scipy import stats mean, std = stats. norm. fit ( np. asarray ( diff_df, dtype = np. float32 )) ci_95 = std * 2 ci_95 17. 75387954711914 Now, I don't trust the da... | deepchem.pdf |
Now, let's identify our active compounds. In my case, this required domain knowledge. Having worked in this area, and having consulted with professors specializing on this channel, I am interested in compounds where the absolute value of the activity is greater than 25. This relates to the desired drug potency we wou... | deepchem.pdf |
dataset_file = 'modulators. csv' task = [ 'active' ] featurizer_func = dc. feat. Conv Mol Featurizer () loader = dc. data. CSVLoader ( tasks = task, feature_field = 'drug', featurizer = featurizer_func ) dataset = loader. create_dataset ( dataset_file ) Lastly, it is often advantageous to numeri... | deepchem.pdf |
Putting Multitask Learning to Work This notebook walks through the creation of multitask models on MUV [1]. The goal is to demonstrate how multitask methods can provide improved performance in situations with little or very unbalanced data. Colab This tutorial and the rest in this sequence are designed to be done in G... | deepchem.pdf |
Congratulations! Time to join the Community! Congratulations on completing this tutorial notebook! If you enjoyed working through the tutorial, and want to continue working with Deep Chem, we encourage you to finish the rest of the tutorials in this series. You can also help the Deep Chem community in the following wa... | deepchem.pdf |
Tutorial Part 13: Modeling Protein-Ligand Interactions By Nathan C. Frey | Twitter and Bharath Ramsundar | Twitter In this tutorial, we'll walk you through the use of machine learning and molecular docking methods to predict the binding energy of a protein-ligand complex. Recall that a ligand is some small mol... | deepchem.pdf |
Looking in indexes: https://pypi. org/simple, https://us-python. pkg. dev/colab-wheels/public/simple/ Requirement already satisfied: deepchem in /usr/local/lib/python3. 10/site-packages (2. 7. 1) Requirement already satisfied: numpy>=1. 21 in /usr/local/lib/python3. 10/site-packages (from deepchem) (1. 24. 3) Requireme... | deepchem.pdf |
data_dir = dc. utils. get_data_dir () dataset_file = os. path. join ( data_dir, "pdbbind_core_df. csv. gz" ) if not os. path. exists ( dataset_file ): print ( 'File does not exist. Downloading file... ' ) download_url ( "https://s3-us-west-1. amazonaws. com/deepchem. io/datasets/pdbbind_core_df... | deepchem.pdf |
We'll use the convenience function nglview. show_mdtraj in order to view our proteins and ligands. Note that this will only work if you uncommented the above cell, installed nglview, and enabled the necessary notebook extensions. v = nglview. show_mdtraj ( ligand_mdtraj ) display ( v ) # interactive view outs... | deepchem.pdf |
the hood, allowing us to get up and running generating poses quickly. vpg = dc. dock. pose_generation. Vina Pose Generator () We could specify a pose scoring function from deepchem. dock. pose_scoring, which includes things like repulsive and hydrophobic interactions and hydrogen bonding. Vina will take care of t... | deepchem.pdf |
We'll show how to download the PDBbind dataset. We can use the loader in Molecule Net to get the 4852 protein-ligand complexes from the "refined" set or the entire "general" set in PDBbind. For simplicity, we'll stick with the ~100 complexes we've already processed to train our models. Next, we'll need a way to trans... | deepchem.pdf |
<timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the corresponding fun ction in deepchem. utils. docking_utils. [15:12:31] UFFTYPER: Warning: hybridization set to SP3 for atom 19 <timed exec>:8: Deprecation Warning: Call to deprecated function prepare_inputs. Please use the ... | deepchem.pdf |
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