# this was downloaded from ACS: https://pubs.acs.org/doi/10.1021/acs.jcim.9b00237
@article{chemprop_theory,
    author = {Yang, Kevin and Swanson, Kyle and Jin, Wengong and Coley, Connor and Eiden, Philipp and Gao, Hua and Guzman-Perez, Angel and Hopper, Timothy and Kelley, Brian and Mathea, Miriam and Palmer, Andrew and Settels, Volker and Jaakkola, Tommi and Jensen, Klavs and Barzilay, Regina},
    title = {Analyzing Learned Molecular Representations for Property Prediction},
    journal = {Journal of Chemical Information and Modeling},
    volume = {59},
    number = {8},
    pages = {3370-3388},
    year = {2019},
    doi = {10.1021/acs.jcim.9b00237},
        note ={PMID: 31361484},
    URL = { 
            https://doi.org/10.1021/acs.jcim.9b00237
    },
    eprint = { 
            https://doi.org/10.1021/acs.jcim.9b00237
    }
}

# this was downloaded from ACS: https://pubs.acs.org/doi/10.1021/acs.jcim.3c01250
@article{chemprop_software,
    author = {Heid, Esther and Greenman, Kevin P. and Chung, Yunsie and Li, Shih-Cheng and Graff, David E. and Vermeire, Florence H. and Wu, Haoyang and Green, William H. and McGill, Charles J.},
    title = {Chemprop: A Machine Learning Package for Chemical Property Prediction},
    journal = {Journal of Chemical Information and Modeling},
    volume = {64},
    number = {1},
    pages = {9-17},
    year = {2024},
    doi = {10.1021/acs.jcim.3c01250},
        note ={PMID: 38147829},
    URL = { 
            https://doi.org/10.1021/acs.jcim.3c01250
    },
    eprint = {     
            https://doi.org/10.1021/acs.jcim.3c01250
    }
}
