Publications

For the most up-to-date list of publications and citation metrics, check out our Google Scholar.

2022

    link Nori, Divya et al. (2022) "De novo PROTAC design using graph-based deep generative models." NeurIPS 2022 AI4Science Workshop.
    link Romeo Atance, Sara et al. (2022) "De novo drug design using reinforcement learning with graph-based deep generative models." J. Chem. Inf. Model. 62(20), 4863ā€“4872.

2021

    link Gao, Wenhao et al. (2022) "Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design." ICLR 2022.
    link Viguera Diez, Juan et al. (2021) "A transferable Boltzmann generator for small-molecule conformers." ELLIS ML4Molecules.
    link Mercado, RocĆ­o et al. (2021) "Exploring graph traversal algorithms in graph-based molecular generation." J. Chem. Inf. Model.
    link Gao, Wenhao et al. (2021) "Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design." arXiv.
    link Romeo Atance, Sara et al. (2021) "De novo drug design using reinforcement learning with graph-based deep generative models." ChemRxiv.
    link Mercado, RocĆ­o et al. (2021) "Exploring graph traversal algorithms in graph-based molecular generation." ChemRxiv.
    link Zhang, Jie et al. (2021). "Comparative study of deep generative models on chemical space coverage." J. Chem. Inf. Model. 61, 6, 2572ā€“2581.

2020

    link Mercado, RocĆ­o et al. (2020). "Practical notes on building molecular graph generative models." Applied AI Letters.
    link Mercado, RocĆ­o et al. (2020). "Graph networks for molecular design." Mach. Learn.: Sci. Technol.
    link Zhang, Jie et al. (2020). "Comparative study of deep generative models on chemical space coverage." ChemRxiv.
    link Mercado, RocĆ­o. (2020). "Using GraphINVENT to generate novel DRD2 actives." Cheminformania.
    link David, Laurianne et al. (2020). "Molecular representations in AI-driven drug discovery: a review and practical guide." J. Cheminf. 12(56).
    link Mercado, RocĆ­o et al. (2020). "Practical notes on building molecular graph generative models." ChemRxiv.
    link Mercado, RocĆ­o et al. (2020). "Graph networks for molecular design." ChemRxiv.

2019

    link Witherspoon, Velencia J. et al. (2019). "Combined nuclear magnetic resonance and molecular dynamics study of methane adsorption in M2(dobdc) metalā€“organic frameworks." J. Phys. Chem. C. 123(19). 12286-12295.

2018

    link Braun, Efrem et al. (2018). "Generating carbon schwarzites via zeolite-templating." PNAS. 115(35). E8116-E8124.
    link Mercado, RocĆ­o et al. (2018). "In silico design of 2D and 3D covalent organic frameworks for methane storage applications." Chem. Mater. 30(15). 5069-5086.
    link Forse, Alexander C. et al. (2018). "Unexpected diffusion anisotropy of carbon dioxide in the metalā€“organic framework Zn2(dobpdc)." J. Am. Chem. Soc. 140(5). 1663-1673.

2016

    link Mercado, RocĆ­o et al. (2016). "Force field development from periodic density functional theory calculations for gas separation applications using metalā€“organic frameworks." J. Phys. Chem. C. 120(23). 12590-12604.

2015

    link Xiang, Zhonghua et al. (2015). "Systematic tuning and multifunctionalization of covalent organic polymers for enhanced carbon capture." J. Am. Chem. Soc. 137(41). 13301-13307.
    link Simon, Cory M. et al. (2015). "Computer-aided search for materials to store natural gas for vehicles." Front. Young Minds. 3-11.
    link Simon, Cory M. et al. (2015). "What are the best materials to separate a xenon/krypton mixture?" Chem. Mater. 27(12). 4459-4475.
    link Simon, Cory M. et al. (2015). "The materials genome in action: identifying the performance limits for methane storage." Energy Environ. Sci. 8. 1190-1199.