ChemoPy: freely available python package for computational biology and chemoinformatics

By Dongsheng Cao, Qingsong Xu, Qian-Nan Hu, Yizeng Liang. 2013

214 citations recorded by the source corpus.

Subfields: Computational Theory and Mathematics, Geometry and Topology, Molecular Biology, Computational Chemistry & Drug Discovery

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computational Theory and Mathematics

1. Sepp Hochreiter
2. Kyle Swanson
3. Thomas Unterthiner
4. Günter Klambauer
5. Andreas Mayr
6. Dongsheng Cao
7. Hongyu Miao
8. Esben Jannik Bjerrum

In Molecular Biology

1. Sepp Hochreiter
2. Thomas Unterthiner
3. Kyle Swanson
4. Günter Klambauer
5. Aiping Lu
6. Ning-Ning Wang
7. Jie Dong
8. Zhi-Jiang Yao

In Computational Chemistry & Drug Discovery

1. Sepp Hochreiter
2. Kyle Swanson
3. Günter Klambauer
4. Thomas Unterthiner
5. Andreas Mayr
6. Dougal Maclaurin

Citations in the corpus

• Message passing neural networks for molecular property prediction (2019, background): “Improvements in property prediction accuracy using these models has primary come through the development of better molecular descriptors [44, 8, 15, 33, 31, 32].”
• ADMETlab: a platform for systematic ADMET evaluation based on a comprehensively collected ADMET database (2018, methodology): “We use Python-RDKit [15], Pybel to wrap molecules; [16] use Chemopy [17] ChemDes [18] and BioTriangle [19] to calculate molecular descriptors and fingerprints; use Scikit-learn to build models of different algorithms; [20] use Numpy [21], Pandas to wrap calculating results into numeric values or fil”
• BioTriangle: a web-accessible platform for generating various molecular representations for chemicals, proteins, DNAs/RNAs and their interactions (2016, methodology): “…database search [49], drug ADME/T prediction [50, 51]) have been implemented for computing molecular descriptors of small molecules, including Dragon, CODESSA, Chemistry Development Kit (CDK) [52], chemopy [53], Molconn-Z, OpenBabel [54], Cinfony [55], Rcpi [56], Indigo, JOELib, Avogadro and RDKit.”
• Toxicity Prediction using Deep Learning (2015, methodology): “We calculated a variety of these descriptors using off-the-shelf software (Cao et al., 2013).”