CV

Education

• PhD in Chemistry and Chemical Engineering, EPFL, 2022-2025 (expected)
• MRes in Drug Discovery and Development, Imperial College London, 2020
• BSc in Chemistry, McGill University, 2019

Research Experience

• Research Intern, Oct. 2023 - Jan. 2024
  • Microsoft AI4Science, Amsterdam
• PhD Researcher, Sep. 2022 - present
  • Schwaller LIAC group, EPFL
• Cheminformatics Graduate Scientist Sep. 2020 - July 2022
  • Molecular AI team, Ola Engkvist, AstraZeneca
• Research Assistant - Chemical Biology Nov. 2019 - Sep. 2020
  • Ed Tate group, Imperial College London
• Drug Innovation Intern May 2019 - Sep. 2019
  • Drug Innovation team, Muthiah Manoharan, Alnylam Pharmaceuticals
• Research Assistant - Nucleic Acid Chemistry May 2018 - Sep. 2018
  • Masad J. Damha group, McGill University
• Research Assistant - Green Chemistry May 2017 - April 2018
  • Chao-Jun Li group, McGill University

Research Interests

• Sample-efficient molecular generative models for drug and catalyst design
• Synthesizable molecular generative design
• Bayesian optimization for chemical reaction optimization and discovery

Supervision

• 2024 | Victoire Lang - Internship, ‘25 BSc Chemistry, EPFL Link

• 2024 | Rémi Schlama - Internship, ‘24 MSc Chemistry, EPFL

• 2023 | Sacha Raffaud - MSc Thesis, ‘23 MSc Applied Computational Science and Engineering, Imperial College London, Link

• 2022 | Christian Josefson & Clara Nyman - MSc Thesis, ‘22 MSc Computer Science and Engineering, Chalmers University of Technology, Link

Publications

^§ denotes equal contribution

DockStream: a docking wrapper to enhance de novo molecular design

J. Guo, J. P. Janet, M. R. Bauer, E. Nittinger, K. A. Giblin, K. Papadopoulos, A. Voronov, A. Patronov, O. Engkvist, C. Margreitter “DockStream: A Docking Wrapper to Enhance De Novo Molecular Design”, J. Cheminform, 2021, 13, 89.

Improving de novo molecular design with curriculum learning

J. Guo^§, V. Fialková^§, J. D. Arango, C. Margreitter, J. P. Janet, K. Papadopoulos, O. Engkvist, A. Patronov “Improving De Novo Molecular Design with Curriculum Learning”, Nat Mach Intell, 2022, 4, 555–563.

Icolos: a workflow manager for structure-based post-processing of de novo generated small molecules

J. H. Moore, M. R. Bauer, J. Guo, A. Patronov, O. Engkvist, C. Margreitter “Icolos: A workflow manager for structure based post-processing of de novo generated small molecules”, Bioinformatics, 2022, 38(21), 4951-4952.

Link-INVENT: Generative Linker Design with Reinforcement Learning

J. Guo^§, F. Knuth^§, C. Margreitter, J. P. Janet, K. Papadopoulos, O. Engkvist, A. Patronov “Link-INVENT: Generative Linker Design with Reinforcement Learning”, Digital Discovery, 2023.

Bayesian Optimization for Chemical Reactions

J. Guo^§, B. Ranković^§, P. Schwaller “Bayesian Optimization for Chemical Reactions”, CHIMIA, 2023, 77, 31-38.

Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design

J. Guo, P. Schwaller “Beam Enumeration: Probabilistic Explainability For Sample Efficient Self-conditioned Molecular Design”, ICLR, 2024.

Sample efficient reinforcement learning with active learning for molecular design

M. Dodds, J. Guo, T. Löhr, A. Tibo, O. Engkvist, J. P. Janet “Sample Efficient Reinforcement Learning with Active Learning for Molecular Design”, Chem. Sci., 2024.

Augmented Memory: Sample-Efficient Generative Molecular Design with Reinforcement Learning

J. Guo, P. Schwaller “Augmented Memory: Capitalizing on Experience Replay to Accelerate De Novo Molecular Design”, JACS Au, 2024.

Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation

J. Guo, P. Schwaller “Saturn: Sample-efficient Generative Molecular Design using Memory Manipulation”, arXiv, 2024.

Machine learning-aided generative molecular design

Y. Du^§, A. R. Jamasb^§, J. Guo^§, T. Fu, C. Harris, Y. Wang, C. Duan, P. Liò, P. Schwaller, T. L. Blundell, Nat Mach Intell, 2024.

Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models

J. Guo, P. Schwaller “Directly Optimizing for Synthesizability in Generative Molecular Design using Retrosynthesis Models”, arXiv, 2024.

It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design

J. Guo, P. Schwaller “It Takes Two to Tango: Directly Optimizing for Constrained Synthesizability in Generative Molecular Design”, arXiv, 2024.