Poranne Research Group


The Poranne Group is a research group working in the field of computational physical organic chemistry. Since October 2021, our group is part of the Schulich Faculty of Chemistry at the Technion—Israel Institute of Technology. We first started working in July 2017 as a sub-group within the laboratory of Prof. Dr. Peter Chen at the Laboratorium für Organische Chemie at the ETH Zürich.

Our work focuses on the investigation of polycyclic aromatic systems and includes characterization of molecular properties, elucidation of structure-property relationships, and illumination of the connection between aromaticity and reactivity in organometallic catalysts. We uncover useful and intuitive connections between structural features and molecular properties, and develop user-friendly pipelines and methods that help connect these abstract properties to real-world synthetic strategies. The chemical insights that we uncover are leveraged to implement machine-learning and deep-learning models for data-driven molecular design and discovery. In addition, we work closely with collaborators around the world to better understand the reactivity and behavior of polycyclic aromatic systems, and to harness their unique properties for various applications.

The group believes in an inclusive and collaborative culture, where team-work and mutual respect are top priorities. We are always open to receiving new members who are excited about learning and who are motivated to work towards advancing our understanding of chemistry and molecular design.

Recent Publications

These are the most recent publications. Clickable titles lead to the version of record. Feel free to email us to request an author's version if you cannot access the publications. Links to preprints are provided, when available. For all publications, please click here.

The COMPAS Project: A Computational Database of Polycyclic Aromatic Systems. Phase 1: cata-Condensed Polybenzenoid Hydrocarbons

Alexandra Wahab, Lara Pfuderer, Eno Paenurk, and Renana Gershoni-Poranne*
Journal of Chemical Information and Modeling, July 2022

Interpretable Deep-Learning Unveils Structure-Property Relationships in Polybenzenoid Hydrocarbons

Tomer Weiss, Alexandra Wahab, Alex M. Bronstein, and Renana Gershoni-Poranne*
The Journal of Organic Chemistry, December 2022 (accepted)

Text-based representations with interpretable machine learning reveal structure–property relationships of polybenzenoid hydrocarbons

Shachar Fite, Alexandra Wahab, Eno Paenurk, Zeev Gross, and Renana Gershoni-Poranne*
The Journal of Physical Organic Chemistry, December 2022

Localized Antiaromaticity Hot-spot Drives Reductive Dehydrogenative Cyclizations in Bis- and Mono-Helicenes

Zheng Zhou, Dominic T. Egger, Chaowei Hu, Matthew Pennachio, Zheng Wei, Rahul K. Kawade, Ökten Üngör, Renana Gershoni-Poranne*, Marina A. Petrukhina*, and Igor V. Alabugin*
Journal of the American Chemical Society, June 2022

Simple and Efficient Visualization of Aromaticity: Bond Currents Calculated from NICS Values

Eno Paenurk* and Renana Gershoni-Poranne*
Physical Chemistry Chemical Physics, January 2022
Highlighted in Chemistry World.

Extensive Redox Non-Innocence in Iron Bipyridine-Diimine Complexes: a Combined Spectroscopic and Computational Study

Ranjeesh Thenarukandiyil, Eno Paenurk, Anthony Wong, Natalia Fridman, Amir Karton, Raanan Carmieli, Gabriel Ménard, Renana Gershoni-Poranne*, and Graham de Ruiter*
Inorganic Chemistry, November 2021

Tuning Magnetic Interactions Between Triphenylene Radicals by Variation of Crystal Packing in Structures with Alkali Metal Counterions

Zheng Zhou, Ökten Üngör, Zheng Wei, Michael Shatruk*, Alexandra Tsybizova, Renana Gershoni-Poranne*, and Marina Petrukhina*
Inorganic Chemistry, September 2021