预测溶解度 (Predicting Solubility)

相关资料 (Related Material)

• Jupyter Notebook for the article

  Jupyter Notebook的文章

• Drug Discovery with Graph Neural Networks — part 2

  图神经网络进行药物发现-第2部分

• Introduction to Cheminformatics

  化学信息学导论

• Deep learning on graphs: successes, challenges, and next steps (article by prof Michael Bronstein)

  图上的深度学习：成功，挑战和下一步 (迈克尔·布朗斯坦教授的文章)

• Towards Explainable Graph Neural Networks

  走向可解释的图形神经网络

目录 (Table of Contents)

• Introduction介绍
• A Special Chemistry Between Drug Development and Machine Learning药物开发与机器学习之间的特殊化学
• Why Molecular Solubility is Important为什么分子溶解度很重要
• Approaching the Problem with Graph Neural Networks图神经网络解决问题
• Hands-on Part with DeepchemDeepchem的动手部分
• About Me关于我

介绍 (Introduction)

This article is a mix of theory behind drug discovery, graph neural networks and a practical part of Deepchem library. The first part will discuss potential applications of machine learning in drug development and then explain what molecular features might prove useful for the graph neural network model. We then dive into coding part and create a GNN model that can predict the solubility of a molecule. Let’s get started!

本文综合了药物发现，图形神经网络和Deepchem库的实际部分的理论知识。 第一部分将讨论机器学习在药物开发中的潜在应用，然后解释什么分子特征可能对图神经网络模型有用。 然后，我们深入编码部分，并创建可以预测分子溶解度的GNN模型。 让我们开始吧！

药物开发与机器学习之间的特殊化学 (A Special Chemistry between Drug Development and Machine Learning)

Drug development is a time-consuming process which might take decades to approve the final version of the drug [1]. It starts from the initial stage of drug discovery where it identifies certain groups of molecules that are likely to become a drug. Then, it goes through several steps to eliminate unsuitable molecules and finally tests them in real life. Important features that we look at during the drug discovery stage are ADME (Absorption, Distribution, Metabolism, and Excretion) properties. We can say that drug discovery is an optimization problem where we predict the ADME properties and choose those molecules that might increase the likelihood of developing a safe drug [2]. Highly efficient computational methods that find molecules with desirable properties speed up the drug development process and give a competitive advantage over other R&D companies.

药物开发是一个耗时的过程，可能需要数十年才能批准该药物的最终版本[1]。 它从药物发现的初始阶段开始，在此阶段它可以识别可能成为药物的某些分子组。 然后，它通过几个步骤来消除不合适的分子，并最终在现实生活中对其进行测试。 重要的特征，我们在药物开发阶段看是ADME(A bsorption，d istribution， 男 etabolism和E xcretion)性能。 可以说，药物发现是一个优化问题，我们可以预测ADME特性并选择可能增加开发安全药物可能性的分子[2]。 查找具有所需特性的分子的高效计算方法可加快药物开发过程，并提供优于其他研发公司的竞争优势。

It was only a matter of time before machine learning was applied to the drug discovery. This allowed to process molecular datasets with a speed and precision that had not been seen before [3]. However, to make the molecular structures applicable to machine learning, many complicated preprocessing steps have to be performed such as converting 3D molecular structures to 1D fingerprint vectors, or extracting numerical features from specific atoms in a molecule.

将机器学习应用于药物发现只是时间问题。 这样可以以前所未有的速度和精度处理分子数据集[3]。 但是，要使分子结构适用于机器学习，必须执行许多复杂的预处理步骤，例如将3D分子结构转换为1D指纹矢量 ，或从分子中的特定原子提取数值特征。

为什么分子溶解度很重要 (Why Molecular Solubility is Important)

One of the ADME properties, absorption, determines whether the drug can reach efficiently the patient’s bloodstream. One of the factors behind the absorption is aqueous solubility, i.e. whether a certain substance is soluble in water. If we are able to predict the solubility, we can also get a good indication of the absorption property of the drug.

ADME的特性之一就是吸收，它决定药物是否可以有效地到达患者的血液中。 吸收背后的因素之一是水溶性，即某种物质是否可溶于水。 如果我们能够预测溶解度，我们也可以很好地表明药物的吸收特性。

图神经网络解决问题 (Approaching the Problem with Graph Neural Networks)

To apply GNNs to molecular structures, we must transform the molecule into a numerical representation that can be understood by the model. It is a rather complicated step and it will vary depending on the specific architecture of the GNN model. Fortunately, most of that preprocessing is covered by external libraries such as Deepchem or RDKit.

要将GNN应用于分子结构，我们必须将分子转换为模型可以理解的数字表示形式。 这是一个相当复杂的步骤，并且会根据GNN模型的特定架构而有所不同。 幸运的是，大多数预处理都被Deepchem或RDKit之类的外部库所覆盖。

Here, I will quickly explain the most common approaches to preprocess a molecular structure.

在这里，我将快速解释预处理分子结构的最常用方法。

微笑 (SMILES)

SMILES is a string representation of the 2D structure of the molecule. It maps any molecule to a special string that is (usually) unique and can be mapped back to the 2D structure. Sometimes, different molecules can be mapped to the same SMILES string which might decrease the performance of the model.

SMILES是分子的2D结构的字符串表示。 它将任何分子映射到(通常)唯一且可以映射回2D结构的特殊字符串。 有时，不同的分子可以映射到相同的SMILES字符串，这可能会降低模型的性能。

指纹识别 (Fingerprints)

Fingerprints is a binary vector where each bit represents whether a certain substructure of the molecule is present or not. It is usually quite long and might fail to incorporate some structural information such as chirality.

指纹是一个二进制向量，其中每个位代表是否存在该分子的某个子结构 。 它通常很长，可能无法合并一些结构信息，例如手性 。

邻接矩阵和特征向量 (Adjacency Matrix and Feature Vectors)

Another way to preprocess a molecular structure is to create an adjacency matrix. The adjacency matrix contains information about the connectivity of atoms, where “1” means that there is a connection between them and “0” that there is none. The adjacency matrix is sparse and is often quite big which might not be very efficient to work with.

预处理分子结构的另一种方法是创建邻接矩阵 。 邻接矩阵包含有关原子连接性的信息，其中“ 1”表示原子之间存在连接，而“ 0”表示不存在连接。 邻接矩阵是稀疏的，并且通常很大，使用它可能不是很有效。

Together with this matrix, we can provide to the GNN model information about each individual atom and information about neighbouring atoms in a form of a vector. In the feature vector for each atom, there can be information about the atomic number, number of valence electrons, or number of single bonds. There is of course many more and they can fortunately be generated by RDKit and Deepchem,

与此矩阵一起，我们可以以矢量的形式向GNN模型提供有关每个单个原子的信息以及有关相邻原子的信息。 在每个原子的特征向量中，可以包含有关原子序数，价电子数或单键数的信息。 当然还有更多，它们可以由RDKit和Deepchem生成，

溶解度 (Solubility)

The variable that we are going to predict is called cLogP and is also known as octanol-water partition coefficient. Basically, the lower is the value the more soluble it is in water. clogP is a log ratio so the values range from -3 to 7 [6].

我们将要预测的变量称为cLogP和 也称为辛醇-水分配系数。 基本上，该值越低，它在水中的溶解度越高。 clogP是对数比率，因此值的范围是-3到7 [6]。

There is also a more general equation describing the solubility logS:

还有一个更通用的方程式描述溶解度logS ：

The problem with that equation is that MP is very difficult to predict from the chemical structure of the molecule [7]. All available solubility datasets contain only cLogP value and this is the value that we are going to predict as well.

该方程式的问题在于， 很难通过分子的化学结构来预测MP [7]。 所有可用的溶解度数据集仅包含cLogP值，这也是我们将要预测的值。

Deepchem的动手部分 (Hands-on Part with Deepchem)

Colab notebook that you can run by yourself is here.

您可以自己运行的Colab笔记本在这里。

Deepchem is a deep learning library for life sciences that is built upon few packages such as Tensorflow, Numpy, or RDKit. For molecular data, it provides convenient functionality such as data loaders, data splitters, featurizers, metrics, or GNN models. From my experience, it is quite troublesome to setup so I would recommend running it on the Colab notebook that I’ve provided. Let’s get started!

Deepchem是用于生命科学的深度学习库，它建立在Tensorflow，Numpy或RDKit等少数软件包的基础上。 对于分子数据，它提供了方便的功能，例如数据加载器，数据拆分器，特征化器，度量或GNN模型。 根据我的经验，设置起来很麻烦，所以我建议在我提供的Colab笔记本上运行它。 让我们开始吧！

Firstly, we will download a Delaney dataset, which is considered as a benchmark for solubility prediction task. We then load the dataset using CSVLoader class and specify a column with cLogP data which is passed into tasks argument. In smiles_field, name of the column with SMILES string have to be specified. We choose a ConvMolFeaturizer which will create input features in a format required by the GNN model that we are going to use.

首先，我们将下载Delaney数据集，该数据集被视为溶解度预测任务的基准。 然后，我们使用CSVLoader类加载数据集，并指定包含cLogP数据的列，该列将传递到task参数。 在smiles_field中，必须指定带有SMILES字符串的列的名称。 我们选择一个ConvMolFeaturizer，它将以我们将要使用的GNN模型所需的格式创建输入要素。

# Getting the delaney dataset
!wget https://raw.githubusercontent.com/deepchem/deepchem/master/datasets/delaney-processed.csv
from deepchem.utils.save import load_from_disk
dataset_file= "delaney-processed.csv"# Loading the data from the CSV file
loader = deepchem.data.CSVLoader(tasks=["ESOL predicted log solubility in mols per litre"], smiles_field="smiles", featurizer=deepchem.feat.ConvMolFeaturizer())
# Featurizing the dataset with ConvMolFeaturizer
dataset = loader.featurize(dataset_file)

Later, we split the dataset using RandomSplitter and divide data into training and validation set. We also use a normalization for y values so they have zero mean and unit standard deviation.

之后，我们使用RandomSplitter分割数据集，并将数据分为训练和验证集。 我们还对y值使用归一化，因此它们的均值和单位标准差为零。

# Splitter splits the dataset # In this case it's is an equivalent of train_test_split from sklearnsplitter = deepchem.splits.RandomSplitter()# frac_test is 0.01 because we only use a train and valid as an exampletrain, valid, _ = splitter.train_valid_test_split(dataset,frac_train=0.7,frac_valid=0.29,frac_test=0.01)# Normalizer will normalize y values in the datasetnormalizer = deepchem.trans.NormalizationTransformer(transform_y=True, dataset=train, move_mean=True)train = normalizer.transform(train)test = normalizer.transform(valid)

In this example, we will use a GraphConvModel as our GNN models. It’s an architecture that was created by Duvenaud, et al. You can find their paper here. There are other GNN models as a part of the Deepchem package such as WeaveModel, or DAGModel. You can find a full list of the models with required featurizers here.

在此示例中，我们将使用GraphConvModel作为我们的GNN模型。 这是Duvenaud等人创建的架构。 您可以在这里找到他们的论文。 Deepchem软件包中还包含其他GNN模型，例如WeaveModel或DAGModel。 您可以在此处找到具有所需功能的所有型号的完整列表。

In this code snippet, a person R2 score is also defined. Simply speaking, the closer this value is to 1, the better is the model.

在此代码段中，还定义了人员R2分数。 简单地说，该值越接近1，模型越好。

# GraphConvModel is a GNN model based on
# Duvenaud, David K., et al. "Convolutional networks on graphs for
# learning molecular fingerprints."
from deepchem.models import GraphConvModel
graph_conv = GraphConvModel(1,batch_size=50,mode="regression")
# Defining metric. Closer to 1 is better
metric = deepchem.metrics.Metric(deepchem.metrics.pearson_r2_score)

Deepchem models use Keras API. The graph_conv model is trained with the fit() function. Here you can also specify the number of epochs. We get the scores with evaluate() function. Normalizer has to be passed here because y values need to be mapped again to the previous range before computing the metric score.

Deepchem模型使用Keras API。 graph_conv模型是使用fit()函数训练的。 在这里，您还可以指定时期数。 我们使用评价()函数获得分数。 必须在此处传递规范化器，因为在计算指标得分之前， y值需要再次映射到先前的范围。

# Fitting the model
graph_conv.fit(train, nb_epoch=10)# Reversing the transformation and getting the metric scores on 2 datasets
train_scores = graph_conv.evaluate(train, [metric], [normalizer])
valid_scores = graph_conv.evaluate(valid, [metric], [normalizer])

And that’s all! You can do much more interesting stuff with Deepchem. They created some tutorials to show what else you can do with it. I highly suggest looking over it. You can find them here.

就这样！ 您可以使用Deepchem做更多有趣的事情。 他们创建了一些教程来展示您还可以做什么。 我强烈建议您仔细检查一下。 您可以在这里找到它们。

Thank you for reading the article, I hope it was useful for you!

感谢您阅读本文，希望对您有所帮助！

关于我 (About Me)

I am an MSc Artificial Intelligence student at the University of Amsterdam. In my spare time, you can find me fiddling with data or debugging my deep learning model (I swear it worked!). I also like hiking :)

我是阿姆斯特丹大学的人工智能硕士研究生。 在业余时间，您会发现我不喜欢数据或调试我的深度学习模型(我发誓它能工作！)。 我也喜欢远足:)

Here are my social media profiles, if you want to stay in touch with my latest articles and other useful content:

如果您想与我的最新文章和其他有用内容保持联系，这是我的社交媒体个人资料：

• Medium

  中

• Linkedin

  领英

• Github

  Github

• Personal Website

  个人网站