CV

You can download a copy of my CV here. Last updated: 25-08-01.

Education

• PhD in Chemistry, University of California, Berkeley, 2018
• BSc in Chemistry, California Institute of Technology, 2013

Research experience

• Assistant professor, Jan 2023 - present
  • AI Lab for Molecular Engineering (AIME), Chalmers
• Postdoctoral associate, Aug 2021 – Dec 2022
  • Coley group, MIT
• Postdoctoral researcher, Oct 2018 – Jul 2021
  • Molecular AI team, AstraZeneca
• PhD researcher, Sep 2013 – Aug 2018
  • MolSim group, UC Berkeley
  • LSMO group, EPFL
• Undergraduate researcher, Apr 2011 – Jul 2013
  • Gray group, Caltech

Research areas

• AI-guided molecular engineering
• Deep molecular generative models
• Multi-target therapeutic modalities
• Targeted protein degradation
• Materials design
• Low-data molecular property prediction and optimization
• Atomistic simulations
• Open-source software development

Theses supervised

For the most recent list of theses and students supervised, please see the list of current students and ‘Alumni’ in my team website, or my most recent CV. As of 2025 I am no longer updating this list here.

• 2024 | Pär Aronsson & Amanda Dehlén, ‘24 MSc Data Science & AI and ‘24 MSc Algorithms, Languages, & Logic, Chalmers University of Technology
• 2024 | Philip Ivers Ohlsson, ‘24 MSc Data Science & AI, Chalmers University of Technology
• 2024 | Jin Ahmad, ‘24 BSc Chemistry, Karlstad University, link
• 2024 | Leo Andrekson, ‘24 MSc Biotechnology, Chalmers University of Technology, link
• 2024 | Anders Källberg, ‘24 MSc Biotechnology, Chalmers University of Technology, link
• 2023 | Mert Yurdakul, ‘23 MSc Data Science & AI, Chalmers University of Technology
• 2023 | Edwin Holst & Preetha Mutharasu, ‘23 MSc Data Science & AI, Chalmers University of Technology, link
• 2023 | Stefano Ribes, ‘23 MSc Data Science & AI, Chalmers University of Technology, link
• 2023 | Supriya Kancharla & Siva Manohar Koki, ‘23 MSc Applied Data Science, University of Gothenburg, link
• 2023 | Kinga Jenei, ‘23 MSc Applied Data Science, University of Gothenburg, link
• 2021 | Sara Romeo Atance & Juan Viguera Diez, ‘21 MSc Complex Adaptive Systems, Chalmers University of Technology, link
• 2021 | Julio Ponte Hernández, ‘21 MSc Computer Science & Engineering, Chalmers University of Technology, link
• 2020 | Tobias Rastemo, ‘20 MSc Computer Science & Engineering, Chalmers University of Technology, link
• 2018 | Rueih-Sheng (Ray) Fu, ‘18 BSc Chemical Engineering, University of California, Berkeley

Awards

• 2025 | Supervisor of the Year Finalist (Top 3), Chalmers Doctoral Students’ Guild
• 2023 | Starting Grant, Swedish Research Council
• 2021 | Reviewer of the Month, Communications Chemistry
• 2020 | Outstanding Reviewer, Machine Learning: Science and Technology, IOP Publishing
• 2015 | Outstanding Graduate Student Instructor, Department of Chemistry, University of California, Berkeley

Publications

The materials genome in action: identifying the performance limits for methane storage

Simon, Cory M. et al. (2015). "The materials genome in action: identifying the performance limits for methane storage." Energy Environ. Sci. 8. 1190-1199.

What are the best materials to separate a xenon/krypton mixture?

Simon, Cory M. et al. (2015). "What are the best materials to separate a xenon/krypton mixture?" Chem. Mater. 27(12). 4459-4475.

Computer-aided search for materials to store natural gas for vehicles

Simon, Cory M. et al. (2015). "Computer-aided search for materials to store natural gas for vehicles." Front. Young Minds. 3-11.

Systematic tuning and multifunctionalization of covalent organic polymers for enhanced carbon capture

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.

Force field development from periodic density functional theory calculations for gas separation applications using metal–organic frameworks

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.

Unexpected diffusion anisotropy of carbon dioxide in the metal–organic framework Zn₂(dobpdc)

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.

In silico design of 2D and 3D covalent organic frameworks for methane storage applications

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.

Computationally-driven investigations towards better gas adsorption materials

Mercado, Rocío. (2018). "Computationally-driven investigations towards better gas adsorption materials." UC Berkeley ProQuest. 10841273.

Generating carbon schwarzites via zeolite-templating

Braun, Efrem et al. (2018). "Generating carbon schwarzites via zeolite-templating." PNAS. 115(35). E8116-E8124.

Combined nuclear magnetic resonance and molecular dynamics study of methane adsorption in M₂(dobdc) metal–organic frameworks

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.

Graph networks for molecular design

Mercado, Rocío et al. (2020). "Graph networks for molecular design." ChemRxiv.

Practical notes on building molecular graph generative models

Mercado, Rocío et al. (2020). "Practical notes on building molecular graph generative models." ChemRxiv.

Molecular representations in AI-driven drug discovery: a review and practical guide

David, Laurianne et al. (2020). "Molecular representations in AI-driven drug discovery: a review and practical guide." J. Cheminf. 12(56).

Using GraphINVENT to generate novel DRD2 actives

Mercado, Rocío. (2020). "Using GraphINVENT to generate novel DRD2 actives." Cheminformania.

Comparative study of deep generative models on chemical space coverage

Zhang, Jie et al. (2020). "Comparative study of deep generative models on chemical space coverage." ChemRxiv.

Graph networks for molecular design

Mercado, Rocío et al. (2020). "Graph networks for molecular design." Mach. Learn.: Sci. Technol.

Practical notes on building molecular graph generative models

Mercado, Rocío et al. (2020). "Practical notes on building molecular graph generative models." Applied AI Letters.

Comparative study of deep generative models on chemical space coverage

Zhang, Jie et al. (2021). "Comparative study of deep generative models on chemical space coverage." J. Chem. Inf. Model.

Exploring graph traversal algorithms in graph-based molecular generation

Mercado, Rocío et al. (2021) "Exploring graph traversal algorithms in graph-based molecular generation." ChemRxiv.

De novo drug design using reinforcement learning with graph-based deep generative models

Romeo Atance, Sara et al. (2021) "De novo drug design using reinforcement learning with graph-based deep generative models." ChemRxiv.

Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design

Gao, Wenhao et al. (2021) "Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design." arXiv.

Exploring graph traversal algorithms in graph-based molecular generation

Mercado, Rocío et al. (2021) "Exploring graph traversal algorithms in graph-based molecular generation." J. Chem. Inf. Model.

A transferable Boltzmann generator for small-molecule conformers

Viguera Diez, Juan et al. (2021) "A transferable Boltzmann generator for small-molecule conformers." ELLIS ML4Molecules.

Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design

Gao, Wenhao et al. (2022) "Amortized tree generation for bottom-up synthesis planning and synthesizable molecular design." ICLR 2022.

De novo drug design using reinforcement learning with graph-based deep generative models

Romeo Atance, Sara et al. (2022) "De novo drug design using reinforcement learning with graph-based deep generative models." J. Chem. Inf. Model.

De novo PROTAC design using graph-based deep generative models

Nori, Divya et al. (2022) "De novo PROTAC design using graph-based deep generative models." NeurIPS 2022 AI4Science Workshop.

Data sharing in chemistry: lessons learned and a case for mandating structured reaction data

Mercado, Rocio et al. (2023) "Data sharing in chemistry: lessons learned and a case for mandating structured reaction data." J. Chem. Inf. Model.

Do Chemformers dream of organic matter? Evaluating a transformer model for multi-step retrosynthesis

Westerlund, Annie et al. (2023) "Do Chemformers dream of organic matter? Evaluating a transformer model for multi-step retrosynthesis." ChemRxiv.

Do Chemformers dream of organic matter? Evaluating a transformer model for multi-step retrosynthesis

Westerlund, Annie et al. (2024) "Do Chemformers dream of organic matter? Evaluating a transformer model for multi-step retrosynthesis." J. Chem. Inf. Model.

Modeling PROTAC degradation activity with machine learning

Ribes, Stefano et al. (2024) "Modeling PROTAC degradation activity with machine learning." arXiv.

Contrastive learning for robust cell annotation and representation from single-cell transcriptomics

Andrekson, Leo et al. (2024) "Contrastive learning for robust cell annotation and representation from single-cell transcriptomics." bioRxiv.

A comprehensive review of emerging approaches in machine learning for de novo PROTAC design

Gharbi, Yossra et al. (2024) "A comprehensive review of emerging approaches in machine learning for de novo PROTAC design." arXiv.

Modeling PROTAC degradation activity with machine learning

Ribes, Stefano et al. (2024) "Modeling PROTAC degradation activity with machine learning." Artificial Intelligence in the Life Sciences.

A comprehensive review of emerging approaches in machine learning for de novo PROTAC design

Gharbi, Yossra et al. (2024) "A comprehensive review of emerging approaches in machine learning for de novo PROTAC design." Digital Discovery.

deCIFer: crystal structure prediction from powder diffraction data using autoregressive language models

Lizak Johansen, Frederik et al. (2025) "deCIFer: crystal structure prediction from powder diffraction data using autoregressive language models." arXiv.

Contrastive learning for robust cell annotation and representation from single-cell transcriptomics

Andrekson, Leo et al. (2025) "Contrastive learning for robust cell annotation and representation from single-cell transcriptomics." bioRxiv.

Enhancing PROTAC ternary complex prediction with ligand information in AlphaFold 3

Erazo, Francisco et al. (2025) "Enhancing PROTAC ternary complex prediction with ligand information in AlphaFold 3." ChemRxiv.

RetroSynFormer: planning multi-step chemical synthesis routes via a decision transformer

Granqvist, Emma et al. (2025) "RetroSynFormer: planning multi-step chemical synthesis routes via a decision transformer." ChemRxiv.

LAGOM: A Transformer-based chemical language model for drug metabolite prediction

Larsson, Sofia et al. (2025). "LAGOM: A Transformer-based chemical language model for drug metabolite prediction." ChemRxiv.

PROTAC-Splitter: A machine learning framework for automated identification of PROTAC substructures

Ribes, Stefano et al. (2025). "PROTAC-Splitter: A machine learning framework for automated identification of PROTAC substructures." ChemRxiv.

Predicting PROTAC-mediated ternary complexes with AlphaFold3 and Boltz-1

Dunlop, Nils et al. (2025). "Predicting PROTAC-mediated ternary complexes with AlphaFold3 and Boltz-1." ChemRxiv.