Chainer Chemistry | 用于化学和生物学的深度学习库 - 程序员大本营

来自 : www.pianshen.com/article/62541 发布时间：2021-03-24

Chainer Chemistry

Chainer Chemistry是一个使用Chainer的化学和生物学深度学习库。

Github地址：https://github.com/pfnet-research/chainer-chemistry手册地址：https://chainer-chemistry.readthedocs.io

该库可帮助您轻松地将深度学习应用于分子结构。例如，可以将机器学习应用于具有化合物输入的毒性分类任务或HOMO水平的回归任务。

支持各种图卷积神经网络

随着图卷积网络的出现，可以使用“图结构”作为输入来应用深度学习。图卷积网络目前正在积极研究中，但是该库还实现了多个网络，其中包括今年刚刚发表的一篇论文。

当前实现了以下模型：

NFP：神经指纹GGNN：门控图神经网络WeaveNet：分子图卷积SchNet：连续滤波器卷积神经网络RSGCN：重归一化频谱图卷积网络RelGCN：关系图卷积网络GAT：图注意力网络GIN：图同构网络MPNN：消息传递神经网络Set2SetGNN-FiLM：具有特征方式线性调制的图神经网络MEGNet：MatErials图形网络CGCNN：晶体图卷积神经网络数据预处理和对研究数据集的支持

该软件的设计使各种数据集可以与一个公共接口一起使用。经常用于研究的数据集可以在库中下载和预处理。

当前支持以下数据集：

QM9 ：B3LYP/6-31G（密度泛函理论），适用于最多由9个C，O，N，F和H原子组成的有机分子总结物理特性（如HOMO / LUMO能级和内部能量）的数据集。Tox21：总结12种不同试验毒性的数据集。MoleculeNetZINC (only 250k dataset)User (own) dataset

诸如材料探索和药物发现等应用领域中，使用分子结构作为输入的模拟占有重要地位。其中，已知诸如DFT（密度泛函方法）之类的模拟方法，其用于高精度地捕获量子力学效应，需要大量的计算，尤其是对于大分子。因此，很难模拟大量有用的新候选分子结构。

机器学习领域中，通过学习到目前为止已经测量和计算的数据来预测未知分子的物理性质的方法正在进行研究。通过使用神经网络，期望可以比量子模拟更快地预测特性值。

当考虑将深度学习应用于化合物时，如何处理其输入/输出成为问题。因为普通的深度学习方法将固定长度的矢量值数据作为输入，而分子结构是可变长度的数据格式，可以具有分支和环，即图。然而，最近，提出了能够处理图结构的图卷积神经网络，并引起了关注。

图卷积神经网络

卷积神经网络通过引入仅使用局部信息进行计算的卷积层，在图像分类，分割和图像生成领域取得了成功。

类似地，图卷积神经网络通过引入图上附近节点的卷积运算，可以处理图结构。

CNN将图像作为输入，而Graph CNN可以将图形结构（分子结构等）作为输入。

以图结构为输入的图卷积神经网络不仅可以广泛应用于分子结构，还可以广泛应用于社交网络和交通网络。例如，将图像卷积应用于图像、知识库、流量预测。

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