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所在平台: Udemy |
课程主页: https://www.udemy.com/course/rdkit-cheminformatics-drug-discovery-in-python/
课程评论：没有评论
**课程名称：** RDKit：Python中的化学信息学与药物发现 **课程概述：** 本课程通过两方面深入讲解RDKit工具包：1. 系统性地探索RDKit的常见模块和功能；2. 完成有意义的真实项目。课程内容以Jupyter Notebook形式，逐步细致地进行讲解，该笔记本文档是一个用户友好的代码编辑器。 **学习内容：** * **分子读写（Reading & Writing Molecules）：** 学习如何读取不同格式的分子文件并将其写出，同时掌握分子清理（molecular sanitization）等重要的RDKit概念。 * **分子对象（Molecules）：** 深入了解RDKit中的分子（Molecule）对象及其相关对象（原子Atom、键Bond、构象Conformer），熟悉RDKit如何表示和处理分子。 * **分子操作（Molecule Operations）：** 学习常见的分子操作，包括添加/移除氢原子、程序化修改分子以及进行子结构匹配。 * **描述符与指纹（Descriptors & Fingerprints）：** 掌握如何使用RDKit计算分子描述符和指纹，了解不同的计算方法和可用的指纹类型。 * **分子绘图（Drawing Molecules）：** 学习如何绘制分子，掌握不同的绘图方法、自定义绘图选项、高亮显示原子/键，以及何时使用何种绘图方法。 * **项目实践（Projects）：** 通过实际项目学习如何整合不同的RDKit概念，以完成化学信息学和药物发现领域的真实项目与工作流程。还将学习如何将RDKit与其它Python包集成，例如：使用RDKit和scikit-learn构建用于虚拟筛选的机器学习模型，以及将RDKit与Pandas包结合进行高级数据分析。项目还将展示如何使用RDKit的算法，如最大共用子结构（MCS）分析、三维构象生成和相似性分析。此外，项目还将涵盖更高级的主题，如基于片段的药物设计，这涉及到条件性地处理和连接片段。
In this course, you will learn the RDKit toolkit in two ways: first by systematically exploring the toolkit's common modules and functionalities, and second by working on meaningful real-life projects. The content is explained step by step with details in Jupyter Notebook, which is a user-friendly code editor.In the Reading & Writing Molecules section, the process of reading different formats and writing them will be explained, in addition to important RDKit concepts such as molecular sanitization.In the Molecules section, the Molecule object in RDKit will be explained alongside related objects (Atom, Bond, and Conformer). This section will make you familiar with how RDKit represents and handles molecules.In the Molecule Operations section, the common operations on molecules will be explained, including adding & removing hydrogens, programmatically modifying molecules, and performing substructure matching.In the Descriptors & Fingerprints section, you will learn how to use RDKit to calculate molecular descriptors and fingerprints, the different methods for calculation, and the available types of fingerprints.In the Drawing Molecules section, you will learn how to draw molecules, the different methods for drawing, how to customize drawing options, how to highlight atoms & bonds, and when to use each drawing method.In the Projects section, you will learn how to combine different RDKit concepts to perform real and meaningful projects and workflows in cheminformatics and drug discovery. You will also learn how to integrate RDKit with other Python packages-for example, how to build machine learning models with RDKit and scikit-learn for virtual screening, and how to use RDKit with the Pandas package for advanced data analysis. The projects will also demonstrate how to use RDKit's algorithms, such as MCS (Maximum Common Substructure) analysis, 3D conformer generation, and similarity analysis. The projects will also cover more advanced topics, such as fragment-based drug design with RDKit, which involves handling and connecting fragments conditionally.
