├── DeepChem Jupyter Notebooks ├── LitDeepChem.ipynb ├── litmatter │ ├── .gitignore │ ├── CONTRIBUTING.md │ ├── LICENSE │ ├── LitDeepChem.ipynb │ ├── LitHF.ipynb │ ├── LitNFFs.ipynb │ ├── README.md │ ├── SPDX.spdx │ ├── TPE.ipynb │ ├── example_files │ │ ├── metrics.csv │ │ └── nvidia-smi.csv │ ├── lit_data │ │ ├── __init__.py │ │ ├── data.py │ │ ├── lm_data.py │ │ ├── molnet_data.py │ │ └── nff_data.py │ ├── lit_models │ │ ├── __init__.py │ │ ├── deepchem_models.py │ │ ├── lit_chemgpt.py │ │ ├── lit_hf.py │ │ ├── lit_nffs.py │ │ └── models.py │ ├── prototyping.ipynb │ ├── requirements.txt │ ├── run.sh │ ├── submit.sh │ ├── tokenizers │ │ ├── pubchem10M_selfiesv2_tokenizer │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer.json │ │ │ └── tokenizer_config.json │ │ └── pubchem10M_tokenizer │ │ │ ├── special_tokens_map.json │ │ │ ├── tokenizer.json │ │ │ └── tokenizer_config.json │ ├── tpe.py │ └── train.py ├── 与PytorchLightning结合.ipynb ├── 从实验数据创建一个高精确度的模型.ipynb ├── 使用 ChemBERTa 变换器进行迁移学习.ipynb ├── 使用 TensorFlow 和 PyTorch 创建模型.ipynb ├── 使用 Trident Chemwidgets 进行交互式模型评估.ipynb ├── 使用 hyperopt 高级模型训练.ipynb ├── 使用DeepChem训练第一个模型.ipynb ├── 使用原子卷积网络建立蛋白质配体相互作用模型.ipynb ├── 使用拆分器.ipynb ├── 分子指纹.ipynb ├── 分子无监督嵌入学习.ipynb ├── 原子对分子的贡献.ipynb ├── 合成可行性评价.ipynb ├── 图卷积的介绍.ipynb ├── 在MNIST数据集上训练一个生成对抗网络.ipynb ├── 处理数据集.ipynb ├── 对 MoleculeNet 的介绍.ipynb ├── 建立蛋白质配体相互作用模型.ipynb ├── 把多任务机器学习模型应用在工作中.ipynb ├── 有条件的生成对抗网络.ipynb ├── 深入分子特征化.ipynb ├── 附件 │ ├── Histogram.png │ ├── InteractiveMolecule.png │ ├── JSME.png │ └── Scatter.png ├── 高斯过程简介.ipynb └── 高级模型训练.ipynb ├── LICENSE ├── README.md ├── _config.yml └── index.md /DeepChem Jupyter Notebooks/LitDeepChem.ipynb: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/Computer-Aided-Drug-Design/AIDD-Tutorial-Files/HEAD/DeepChem Jupyter Notebooks/LitDeepChem.ipynb -------------------------------------------------------------------------------- /DeepChem Jupyter 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