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)","metadata":{"id":"mK2nmgSkYE3R"}},{"cell_type":"markdown","source":"**DeepChem is a python library for machine learning & deep learning on molecular & quantum datasets. It is built on top of Pytorch & other popular ML frameworks. It makes it easy to apply ML in production, by providing easy-to-use model export and deployment APIs.**\n\n![download.png](data:image/png;base64,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)","metadata":{"id":"fw5PNFkoOkeL"}},{"cell_type":"markdown","source":"# Installing Required Libraries","metadata":{"id":"6A1cojsjOVVq"}},{"cell_type":"code","source":"!pip install --pre deepchem[tensorflow]","metadata":{"id":"yMhar7F2CHzg","outputId":"395d066d-59aa-4d31-f1c6-71a8648068a6"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Importing Libraries","metadata":{"id":"6RLQLm-6Oc45"}},{"cell_type":"code","source":"import deepchem as dc\nprint(dc.__version__)","metadata":{"id":"pKjMM7WUChli","outputId":"03baed7c-6a72-4718-ee4f-9a263d78ddba"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np","metadata":{"id":"dsDgQK5PDBd3"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Predicting the toxicity of molecules","metadata":{"id":"EQ5yHy5ZEBHZ"}},{"cell_type":"markdown","source":"**In this tutorial, we will demonstrate how to use DeepChem to train a model to predict the toxicity of molecules. DeepChem maintains a module called dc.molnet (short for MoleculeNet)\nthat contains a number of preprocessed datasets for use in machine learning experimentation.**","metadata":{"id":"KmrpbPPnV3iZ"}},{"cell_type":"code","source":"#loading deepchem toxicity datasets\ntox21_tasks, tox21_datasets, transformers = dc.molnet.load_tox21()","metadata":{"id":"-CuW6lNoDEJ_","outputId":"a692ef1b-5783-47ab-9637-377d0aa18985"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**The process of featurization is how a dataset containing information about molecules is transformed into matrices and vectors for use in machine learning analyses.**","metadata":{"id":"oVZsjmdVaS5Q"}},{"cell_type":"code","source":"tox21_tasks","metadata":{"id":"zfNIQVY2GEsx","outputId":"949a0581-eb65-4e2b-d310-9ad98aa61672"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"len(tox21_tasks)","metadata":{"id":"uzW_H97FKcKS","outputId":"9439b0dc-c892-423e-c727-3d9ee05add0b"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Each of the 12 tasks here corresponds with a particular biological experiment. In this case, each of these tasks is for an enzymatic assay which measures whether the molecules in the Tox21 dataset bind with the biological target. The terms NR-AR and so on correspond with these targets. In this case, each of these targets is a particular enzyme believed to be linked to toxic responses to potential therapeutic molecules.**","metadata":{"id":"hDGuQAL5Zbwg"}},{"cell_type":"code","source":"tox21_datasets","metadata":{"id":"L-2oj9zIN3RL","outputId":"4b1f2b83-67de-4a4b-f45b-2a63847ddb79"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_dataset, valid_dataset, test_dataset = tox21_datasets","metadata":{"id":"HLVnxLjmTzvZ"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_dataset.X.shape","metadata":{"id":"lZ2f_FocT9Ho","outputId":"e34bd70d-b0b9-44af-d6d2-005b04955708"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"valid_dataset.X.shape","metadata":{"id":"2EzJxqQMVHzp","outputId":"3dbffa0c-0175-4cdc-c989-d77f1ae17c9e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_dataset.X.shape","metadata":{"id":"QT3R9ZzsVLho","outputId":"7c2952c9-8816-4906-8e95-8d9cf169952e"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.shape(train_dataset.y)","metadata":{"id":"mqSyUCY9VjFm","outputId":"56e9a604-c68e-486f-fe20-160052183c42"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" np.shape(valid_dataset.y)","metadata":{"id":"nTEthJZNVnR2","outputId":"2a39fd53-c2a7-4259-a429-ed5450db0062"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" np.shape(test_dataset.y)","metadata":{"id":"9dZI6At7VnJm","outputId":"6641ce99-825b-467e-8989-cbd518b3f6be"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**There are 12 data points, also called labels, for each sample. In this particular dataset, the samples correspond to molecules, the tasks correspond to biochemical assays, and each label is the result of a\nparticular assay on a particular molecule. Those are what we want to train our model to predict.**","metadata":{"id":"_EA-JlBAFaIb"}},{"cell_type":"markdown","source":"*Finding out how many labels have actually been measured in our datasets*","metadata":{"id":"_e2EecqOGqJS"}},{"cell_type":"code","source":"train_dataset.w.shape","metadata":{"id":"ssR0GP4lZRog","outputId":"06a03c5d-a857-4634-9d78-b5ed8b8ab8f2"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.count_nonzero(train_dataset.w)","metadata":{"id":"9JFOAwsnZReU","outputId":"9faaa8d4-565c-45f9-9136-ab32407ad7b9"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":" np.count_nonzero(train_dataset.w == 0)","metadata":{"id":"Anbk4wzzKgSq","outputId":"2e8384ef-ebc8-4060-e5de-436af6a8e193"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"*Now, we will examine transformers, the final output that was returned by\nload_tox21(). A transformer is an object that modifies a dataset in some way. DeepChem provides many transformers that manipulate data in useful ways. The dataloading routines found in MoleculeNet always return a list of transformers that have been applied to the data, since you may need them later to **untransform** the data.*","metadata":{"id":"zKRZCCVlbXg7"}},{"cell_type":"code","source":"transformers","metadata":{"id":"SyDR0TAdzUG6","outputId":"bc2cf9db-914a-4753-c145-f7f377b4e039"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Now we will show how to build a Multi Task Classifier using DeepChem, where,  where there are multiple labels for every sample and suitable for our Tox21 datasets.**","metadata":{"id":"weWrreoNghh7"}},{"cell_type":"code","source":"model = dc.models.MultitaskClassifier(n_tasks=12, n_features=1024, layer_sizes=[1000])","metadata":{"id":"QaR7j36Wct8D"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**n_tasks is the number of tasks, and n_features is the number of input features for each sample. As we saw earlier, the Tox21 dataset has 12 tasks and 1,024 features for each sample. layer_sizes is a list that sets the number of fully connected hidden layers in the network, and the width of each one. In this case, we specify that there is a single hidden layer of width 1,000.**\n","metadata":{"id":"7bioyEvxhKIp"}},{"cell_type":"code","source":"#fitting the model\nmodel.fit(train_dataset, nb_epoch=10)","metadata":{"id":"FGo46Qlgg__L","outputId":"84c3f36b-4c0e-45c9-b73e-1c135231c593"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"metric = dc.metrics.Metric(dc.metrics.roc_auc_score, np.mean)","metadata":{"id":"-n8nYEi4hhiU"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_scores = model.evaluate(train_dataset, [metric], transformers)\ntest_scores = model.evaluate(test_dataset, [metric], transformers)","metadata":{"id":"UmasItjuhtii"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(train_scores)\nprint(test_scores)","metadata":{"id":"HpcwgnP-hwHC","outputId":"726052f2-95a0-4b5d-a9a0-ce704940195f"},"execution_count":null,"outputs":[]}]}