For the most up-to-date list of publications and citation metrics, check out our Google Scholar.
2025
link Ohlsson, Philip Ivers et al. (2025) "Prediction of Permeability and Efflux Using Multitask Learning." ACS Omega.
link Dunlop, Nils & Erazo, Francisco et al. (2025) "Predicting PROTAC-Mediated Ternary Complexes with AlphaFold3 and Boltz-1." Digital Discovery. (pre-print)
link Larsson, Sofia & Carlsson, Miranda et al. (2025) "LAGOM: A Transformer-Based Chemical Language Model for Drug Metabolite Prediction." AILSCI. (pre-print)
pre-print Kazemi-Khasragh, Elaheh et al. (2025) "Descriptor and Graph-based Molecular Representations in Prediction of Copolymer Properties Using Machine Learning." arXiv.
link Duval, Alexander et al. (2025) "LeMat-GenBench: Bridging the Gap Between Crystal Generation and Materials Discovery." AI4Mat @ NeurIPS 2025.
link Martínez Crespo, Pablo et al. (2025) "TopoMole: Topological Message Passing Meets Hyperedge Messages." AI4Mat @ NeurIPS 2025.
link Cropsal, Télio & Mercado, Rocío. (2025) "Compressing Biology: Evaluating the Stable Diffusion VAE for Phenotypic Drug Discovery." Imageomics @ NeurIPS 2025.
pre-print Ribes, Stefano et al. (2025) "PROTAC-Splitter: A Machine Learning Framework for Automated Identification of PROTAC Substructures." ChemRxiv.
pre-print Granqvist, Emma et al. (2025) "RetroSynFormer: Planning multi-step chemical synthesis routes via a Decision Transformer." ChemRxiv.
pre-print Andrekson, Leo et al. (2025) "Contrastive Learning for Robust Cell Annotation and Representation from Single-Cell Transcriptomics." bioRxiv .
pre-print 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." AILSCI. 6, 100114. (pre-print)
link Gharbi, Yossra at al. (2024) "A Comprehensive Review of Emerging Approaches in Machine Learning for De Novo PROTAC Design." Digital Discovery ASAP.
pre-print Andrekson, Leo et al. (2024) "Contrastive Learning for Robust Cell Annotation and Representation from Single-Cell Transcriptomics." bioRxiv .
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
pre-print 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." AI4Science @ NeurIPS 2022.
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. (pre-print)
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. (pre-print)
pre-print Romeo Atance, Sara et al. (2021) "De novo drug design using reinforcement learning with graph-based deep generative models." 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. (pre-print)
link Mercado, Rocío et al. (2020). "Graph networks for molecular design." Mach. Learn.: Sci. Technol. (pre-print)
pre-print 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).
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.