/graph-kernel-SVM-for-toxicity-of-pesticides-to-bees

reproduce our bee toxicity paper DOI: 10.1063/5.0090573

Primary LanguageJulia

Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels

Python and Julia code to reproduce the results of our paper:

P. Yang, A. Henle, X. Fern, C. Simon. Classifying the toxicity of pesticides to honey bees via support vector machines with random walk graph kernels. The Journal of Chemical Physics. 2022. DOI: 10.1063/5.0090573. link

🐝 the raw data from the BeeTox AI project is in the folder, BeeToxAI Data/.

🐝 the folder /src contains our source code for the random walk graph kernel.

🐝 to reproduce our results:

  • run python RDkit_smiles_convert.py to convert the SMILES strings used in the BeeToxAI project into Daylight SMILES that can be parsed by MolecularGraph.jl.
  • run julia compute_Gram_matrices.jl to pre-compute the Gram matrices, giving the L-RWGK between each pesticide molecule for different L.
  • run python fingerprint.py to compute the MACCS fingerprints.
  • open kSVM_beetox.jl in Pluto to train and evaluate the SVMs and produce the plots in the paper.

other scripts useful to us but likely not as useful to others:

🐝 the Julia scripts test_run_time.jl and runtime_plot.jl are to assess the run time.

🐝 open toy_svm.jl in Pluto to make a toy SVM visualization for talks.

🐝 python viz_MACCS.py makes visualizations of the molecules with substructures activating bits in the MACCS fingerprints.

🐝 open viz_for_paper.jl in Pluto to make visualizations of graphs we used to illustrate concepts in the paper.