lindemann • Rank 9.1 • Science 77%

lindemann is a python package to calculate the Lindemann index of a lammps trajectory

xyz2graph • Rank 10.2 • Science 67%

Convert an xyz file into a molecular graph and create a 3D visualisation of the graph.

optimade • Rank 8.9 • Science 67%

Isomorphic TypeScript / JavaScript client to aggregate all the official Optimade providers

xbcf • Rank 2.3 • Science 57%

Predicting BCF values with explanations

pmapper • Rank 13.1 • Science 26%

3D pharmacophore signatures and fingerprints

pytoda • Rank 12.9 • Science 13%

pytoda - PaccMann PyTorch Dataset Classes. Read the docs: https://paccmann.github.io/paccmann_datasets/

pharmd • Rank 6.4 • Science 13%

MD pharmacophores and virtual screening

https://github.com/cbouy/m2-chemoinformatics • Science 23%

Scripts I used during my Master in Chemoinformatics in Strasbourg

molecule-signature • Science 44%

Signature based molecule enumeration from morgan fingerprints

https://github.com/blueobelisk/blueobelisk.github.io • Science 26%

The website.

https://github.com/cbouy/molhighlighter • Science 13%

Multicolored substructure highlights made easy

mzkit • Science 49%

Data toolkits for processing NMR, MALDI MSI, MALDI single cell, Raman Spectroscopy, LC-MS and GC-MS raw data, chemoinformatics data analysis and data visualization.

marsilea • Science 39%

Declarative creation of composable visualization for Python (Complex heatmap, Upset plot, Oncoprint and more~)

global-chem • Science 57%

A Knowledge Graph of Common Chemical Names to their Molecular Definition

ramanspectrumpredictor_qm9 • Science 26%

Predict Raman spectra of organic molecules with our ML pipeline using RDKit descriptors and a QM9-style dataset. 🌟🔍 Explore the project on GitHub!

https://github.com/cdk/chempyformatics • Science 13%

chemfetchtool • Science 67%

ChemFetchTool is a web-based tool employing the PubChem PUG REST API endpoint for automated retrieval of molecular properties given only the compound name(s)

molecule-signature-paper • Science 54%

Code supporting the paper 'Getting Molecules from their Fingerprints: Generative Models vs. Deterministic Enumeration'