RDKit toolkit实战三：描述符计算及可视化

来源：互联网发布：hosts网络源每日更新编辑：程序博客网时间：2024/05/16 11:13

Descriptor Calculation & Visualization of Descriptors

Linux（CentOS 7_x64位）系统下安装RDkit(修正)点击打开链接

RDKit toolkit实战演练学习一下，参考网站点击打开链接

描述符计算在结构搜索比对以及QSAR中应用很广。

#!Python2.7from rdkit import Chemfrom rdkit.Chem import Descriptorsfrom rdkit.Chem import AllChemfrom rdkit.Chem.Draw import SimilarityMapsm = Chem.MolFromSmiles('c1ccccc1C(=O)O')Descriptors.TPSA(m)m = Chem.MolFromSmiles('c1ccccc1C(=O)O')AllChem.ComputeGasteigerCharges(m)float(m.GetAtomWithIdx(0).GetProp('_GasteigerCharge'))get_ipython().run_line_magic('matplotlib', 'inline')mol = Chem.MolFromSmiles('COc1cccc2cc(C(=O)NCCCCN3CCN(c4cccc5nccnc54)CC3)oc21')AllChem.ComputeGasteigerCharges(mol)contribs = [float(mol.GetAtomWithIdx(i).GetProp('_GasteigerCharge')) for i in range(mol.GetNumAtoms())]fig = SimilarityMaps.GetSimilarityMapFromWeights(mol, contribs, colorMap='jet', contourLines=10)from rdkit.Chem import rdMolDescriptorscontribs = rdMolDescriptors._CalcCrippenContribs(mol)fig = SimilarityMaps.GetSimilarityMapFromWeights(mol,[x for x,y in contribs], colorMap='jet', contourLines=10)

Jupyter Notebooks效果

阅读全文

0 0