Publications

Jeff Guo, Vendy Fialková, Juan Diego Arango, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, Atanas Patronov (2022). Improving De Novo Molecular Design with Curriculum Learning. Nature Machine Intelligence.

Jon Paul Janet (2022). Data-Driven Mapping of Inorganic Chemical Space for the Design of Transition Metal Complexes and Metal-Organic Frameworks. ACS Symposium Series.

David Buterez, Jon Paul Janet, Steven Kiddle, Pietro Liò (2022). Multi-fidelity machine learning models for improved high-throughput screening predictions. ChemRxiv.

Jeff Guo, Franziska Knuth, Christian Margreitter, Jon Paul Janet, Kostas Papadopoulos, Ola Engkvist, Atanas Patronov (2022). Link-INVENT: Generative Linker Design with Reinforcement Learning. ChemRxiv.

Jeff Guo, Jon Paul Janet, Matthias R. Bauer, Eva Nittinger, Kathryn A. Giblin, Kostas Papadopoulos, Alexey Voronov, Atanas Patronov, Ola Engkvist, Christian Margreitter (2021). DockStream: A Docking Wrapper to Enhance De Novo Molecular Design. Journal of Cheminformatics.

Kostas Papadopoulos, Kathryn Giblin, Jon Paul Janet, Atanas Patronov, Ola Engkvist (2021). De novo design with deep generative models based on 3D similarity scoring. Bioorganic & Medicinal Chemistry.

Jon Paul Janet, Anna Tomberg, Jonas Boström (2021). Reusability report: Learning the language of synthetic methods used in medicinal chemistry. Nature Machine Intelligence.

Danial R. Harper, Aditya Nandy, Naveen Arunachalam, Chenru Duan, Jon Paul Janet, Heather J. Kulik (2021). Representations and strategies for transferable machine learning improve model performance in chemical discovery. The Journal of Chemical Physics.

Jon Paul Janet, Aditya Nandy, Chenru Duan, Fang Liu, Heather J. Kulik (2021). Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design. Accounts of Chemical Research.

Jon Paul Janet, Heather J. Kulik (2020). Machine Learning in Chemistry. ACS in Focus.

Jon Paul Janet, Sahasrajit Ramesh, Chenru Duan, Heather J. Kulik (2020). Accurate Multiobjective Design in a Space of Millions of Transition Metal Complexes with Neural-Network-Driven Efficient Global Optimization. ACS Central Science.

Michael Taylor, Tzuhsiung Yang, Sean Lin, Aditya Nandy, Jon Paul Janet, Chenru Duan, Heather J. Kulik (2020). Seeing is Believing: Experimental Spin States from Machine Learning Model Structure Predictions. The Journal of Physical Chemistry A.

Aditya Nandy, Jiazhou Zhu, Jon Paul Janet, Chenru Duan, Rachel B. Getman, Heather J. Kulik (2019). Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal-Oxo Intermediate Formation. ACS Catalysis.

Stefan Gugler, Jon Paul Janet, Heather J. Kulik (2019). Enumerating de novo small inorganic complexes for machine learning and chemical discovery. Molecular Systems Design & Engineering.

Jon Paul Janet, Chenru Duan, Tzuhsiung Yang, Aditya Nandy, Heather J. Kulik (2019). A quantitative uncertainty metric controls error in neural network-driven chemical discovery. Chemical Science.

Jon Paul Janet, Fang Liu, Aditya Nandy, Chenru Duan, Tzuhsiung Yang, Sean Lin, Heather J. Kulik (2019). Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry. Inorganic Chemistry.

Chenru Duan, Jon Paul Janet, Fang Liu, Aditya Nandy, Heather J. Kulik (2019). Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models. Journal of Chemical Theory and Computation.

Aditya Nandy, Chenru Duan, Jon Paul Janet, Stefan Gugler, Heather J. Kulik (2018). Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry. Industrial & Engineering Chemistry Research.

Jon Paul Janet, Lydia Chan, Heather J. Kulik (2018). Accelerating Chemical Discovery with Machine Learning: Simulated Evolution of Spin Crossover Complexes with an Artificial Neural Network. The Journal of Physical Chemistry Letters.

Jon Paul Janet, Heather J. Kulik (2017). Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure–Property Relationships. The Journal of Physical Chemistry A.

Jon Paul Janet, Heather J. Kulik (2017). Predicting electronic structure properties of transition metal complexes with neural networks. Chemical Science.

Jon Paul Janet, Terry Z. H. Gani, Adam H. Steeves, Efthymios I. Ioannidis, Heather J. Kulik (2017). Leveraging Cheminformatics Strategies for Inorganic Discovery: Application to Redox Potential Design. Industrial & Engineering Chemistry Research.

Jon Paul Janet, Qing Zhao, Efthymios I Ioannidis, Heather J Kulik (2017). Density functional theory for modelling large molecular adsorbate--surface interactions: a mini-review and worked example. Molecular Simulation.

Akash Bajaj, Jon Paul Janet, Heather J. Kulik (2017). Communication: Recovering the flat-plane condition in electronic structure theory at semi-local DFT cost. The Journal of Chemical Physics.

Jon Paul Janet, Yixiang Liao, Dirk Lucas (2015). Heterogeneous nucleation in CFD simulation of flashing flows in converging--diverging nozzles. International Journal of Multiphase Flow.

Jon Paul Janet, Kevin Harding, Craig Sheridan, Dianne; Drake (2015). Conceptual Project on Eliminating Acid Mine Drainage (AMD) by Directed Pumping. ICARD - IMWA 2015.

Jon Paul Janet, Kevin Harding, Craig Sheridan, Dianne; Drake (2014). Increasing Pumping Depth in the Long-term Management of Acid Mine Drainage. WISA 2014: Water Institute of Southern Africa.