Publications

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

2025

    link Granqvist, Emma et al. (2025) "RetroSynFormer: Planning multi-step chemical synthesis routes via a Decision Transformer." ChemRxiv.
    link Erazo, Francisco et al. (2025) "Enhancing PROTAC Ternary Complex Prediction with Ligand Information in AlphaFold 3." ChemRxiv.
    link Andrekson, Leo et al. (2025) "Contrastive Learning for Robust Cell Annotation and Representation from Single-Cell Transcriptomics." bioRxiv .
    link Lizak Johansen, Frederik et al. (2025) "deCIFer: Crystal Structure Prediction from Powder Diffraction Data using Autoregressive Language Models." arXiv.

2024

    link Ribes, Stefano et al. (2024) "Modeling PROTAC Degradation Activity with Machine Learning." Artif. Intell. Life Sci. 6, 100114.
    link Gharbi, Yossra at al. (2024) "A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design." Digital Discovery ASAP.
    link Andrekson, Leo et al. (2024) "Contrastive Learning for Robust Cell Annotation and Representation from Single-Cell Transcriptomics." bioRxiv .
    link Ribes, Stefano et al. (2024) "Modeling PROTAC Degradation Activity with Machine Learning." arXiv .
    link Westerlund, Annie M. et al. (2024) "Do Chemformers dream of organic matter? Evaluating a transformer model for multi-step retrosynthesis." J. Chem. Inf. Model. 64, 8, 3021–3033.

2023

    link Westerlund, Annie M. et al. (2023) "Do Chemformers dream of organic matter? Evaluating a transformer model for multi-step retrosynthesis." ChemRxiv.
    link Mercado, Rocío et al. (2023) "Data sharing in chemistry: lessons learned and a case for mandating structured reaction data." J. Chem. Inf. Model. 63, 14, 4253–4265.

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), 4863–4872.

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, Rocío 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, Rocío 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, 2572–2581.

2020

    link Mercado, Rocío et al. (2020). "Practical notes on building molecular graph generative models." Applied AI Letters.
    link Mercado, Rocío 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, Rocío. (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, Rocío et al. (2020). "Practical notes on building molecular graph generative models." ChemRxiv.
    link Mercado, Rocío 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) metal–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, Rocío 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 metal–organic framework Zn2(dobpdc)." J. Am. Chem. Soc. 140(5). 1663-1673.

2016

    link Mercado, Rocío et al. (2016). "Force field development from periodic density functional theory calculations for gas separation applications using metal–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.