"RDKit | Chemical Informatics and AI" column describes
RDKit introduce relevant knowledge and use of the application as well as RDKit treatment chemical, biological, pharmaceutical and materials science in molecular data can be entered as an important tool for machine learning and deep learning model. Covers based reader molecule RDKit of Python3 of DNA fingerprinting and molecular descriptors compound was calculated, 2D / 2D comparison compound, Compound similarity search, compound skeleton analysis and sub-structure search, the RMSD calculation and conformational generating optimized , molecules with similar cluster analysis, chemical processing, visualization and space exploration, chemical and related RDkit machine learning, deep learning process detailed application.
"RDKit | Chemical Informatics and AI" Column Address: https://blog.csdn.net/u012325865/category_9278913.html
RDKit Profile
Open source chemical informatics toolkit
- RDKit in 2000-- during 2006 in the development and use Rational Discovery for building absorption, distribution, metabolism, metabolism, toxicity prediction models and biological activity.
- In June 2006 Rational Discovery was closed, but the toolkit as open source under the BSD license issued.
- Currently, RDKit positive contribution to the open source development by Novartis, including the Novartis donated the source code.
RDKit Features
- Business-friendly BSD license
- The core data structures and algorithms written in C ++
- Boost.Python generated using Python 3.x wrappers
- With SWIG generated Java and C # wrapper
- 2D and 3D molecular manipulation
- DNA fingerprinting and molecular machine learning and learning descriptor generation depth
- Molecular PostgreSQL database integration
- KNIME chemical informatics Node
RDKit提供各种功能,如不同的化学I/O格式,包括SMILES/SMARTS,结构数据格式(SDF),Thor数据树(TDT),Sybyl线符号(SLN),mol2和蛋白质结构文件(PDB)。子结构搜索; 标准SMILES; 手性支持;化学转化;化学反应;分子序列化;相似性/多样性选择;二维药效团;三维维药效团;分层子图/片段分析; Bemis和Murcko骨架;逆合成组合分析及分子碎裂(RECAP); 多分子最大共同亚结构;功能图;基于形状的相似性;基于RMSD的分子比对;基于形状的对齐;使用Open3-DALIGN算法的无监督分子-分子比对;与PyMOL进行3D可视化集成;功能基团过滤;分子描述符库;相似图;机器学习等等
RDKit安装
Linux(CentOS 7_x64位)系统下安装RDkit
Linux(CentOS 7_x64位)系统下安装RDkit(修正)
基于RDKit的分子读写及入门
RDKit | 基于RDKit输出分子结构图(Image)的方法
基于RDKit的分子指纹与描述符计算
分子指纹
RDKit:化学指纹(Chemical Fingerprinting)
分子描述符
RDKit | 基于RDKit描述三维分子形状(3D描述符)
基于RDKit与Python3的构象与RMSD计算
基于RDKit与Python3的相似性与分子图
RDKit:化合物亚结构(Substructure)搜索(基于Python3)
RDKit toolkit实战二:Generating Similarity Maps Using Fingerprints
RDKit与药效团
RDKit | 聚类分析与可视化
RDKit与化学反应
RDKit | 基于RDKit通过SMARTS定义反应模式来生成反应产物
RDKit与PostgreSQL
CentOS 7 源码编译安装 PostgreSQL 11.2
基于RDKit与Python的应用
RDKit | 通过评估合成难度(SA Score)筛选化合物
RDKit | 天然产物的相似度评分(NP-likeness)
基于RDKit的Python脚本:SDF格式转SMILES格式
RDKit与AI、深度学习和机器学习
药物设计的深度学习(Deep Learning for Drug Design)
RDKit | 基于Pytorch和RDKit建立QSAR模型
RDkit&mol2vec :基于逻辑回归的靶标抑制剂活性二分类对比
RDKit | 基于随机森林(RF)的机器学习模型预测hERG阻断剂活性
RDKit | 基于不同描述符和指纹的机器学习模型预测logP
RDKit | 基于RDKit和scikit-learn的KNN模型预测Ames的致突变性
RDKit | 基于化合物结构式图像估算分子式(OpenCV、CNN)