CV

You can download a copy of my CV here. Last updated: 24-09-16.

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 (GenAI)
Multi-target therapeutic modalities
Targeted protein degradation
Low-data molecular property prediction and optimization
Atomistic simulations
Open-source software development

Theses supervised

2024 | Pär Aronsson & Amanda Dehlén, ‘24 MSc Data Science & AI and ‘24 MSc Algorithms, Languages, & Logic, Chalmers University of Technology, link
2024 | Philip Ivers Ohlsson, ‘24 MSc Data Science & AI, Chalmers University of Technology, link
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, link
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

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.