Professor Renana Gershoni-Poranne
Technion-Israel Institute of Technology,
Technion City, Haifa 32000 Israel
Polycyclic aromatic systems (PASs) are among the most prevalent and impactful classes of compounds in the natural and man-made world. Though aromatic systems have captured the fascination of chemists for almost two centuries, a general conceptual framework for understanding and predicting the structure-property relationships of polycyclic systems remains elusive. Yet, the structure-property relationships of PBHs have both conceptual and practical implications and understanding them can enable design of new functional compounds. We address this gap using a combination of computational chemistry and data science tools.
We first interrogated polybenzenoid hydrocarbons using a combination of traditional computational techniques, including characterization of their aromatic character in the S0 and T1 states (described with the NICS metric), their spin density in the T1 state, and their S0—T1 energy gaps. Regularities were revealed that allowed for simple and intuitive design guidelines to be defined.1
To verify these guidelines in a data-driven manner, we generated a new database – the COMPAS Project2 and developed two types of molecular representation to enable machine- and deep-learning models to train on the new data: a) a text-based representation3 and b) a graph-based representation.4
In addition to their predictive ability, we demonstrate the interpretability of the models that is achieved when using these representations. The extracted insight in some cases confirms well-known “rules of thumb” and in other cases disproves common wisdom and sheds new light on this classical family of compounds.
Finally, we implemented a generative model that design novel PASs with targeted properties in an effective and efficient manner, demonstrating the first inverse design of PASs.5
Keywords:
References
(1) Markert, G.; Paenurk, E.; Gershoni‐Poranne, R. Prediction of Spin Density, Baird-Antiaromaticity, and Singlet–Triplet Energy Gap in Triplet-State Polybenzenoid Systems from Simple Structural Motifs. Chem. Eur. J. 2021, 27, 1–14.
https://doi.org/10.1002/chem.202100464.(2) Wahab, A.; Pfuderer, L.; Paenurk, E.; Gershoni-Poranne, R. The COMPAS Project: A Computational Database of Polycyclic Aromatic Systems. Phase 1: Cata-Condensed Polybenzenoid Hydrocarbons. J. Chem. Inf. Model. 2022, 62 (16), 3704.
https://doi.org/10.1021/acs.jcim.2c00503.(3) Fite, S.; Wahab, A.; Paenurk, E.; Gross, Z.; Gershoni-Poranne, R. Text-Based Representations with Interpretable Machine Learning Reveal Structure-Property Relationships of Polybenzenoid Hydrocarbons. Journal of Physical Organic Chemistry 2022, e4458.
https://doi.org/10.1002/poc.4458.(4) Weiss, T.; Wahab, A.; Bronstein, A. M.; Gershoni-Poranne, R. Interpretable Deep-Learning Unveils Structure-Property Relationships in Polybenzenoid Hydrocarbons. 2022.
https://doi.org/10.26434/chemrxiv-2022-krng1.(5) Weiss, T.; Cosmo, L.; Yanes, E. M.; Chakraborty, S.; Bronstein, A. M.; Gershoni-Poranne, R. Guided Diffusion for Inverse Molecular Design. ChemRxiv April 5, 2023.
https://doi.org/10.26434/chemrxiv-2023-z8ltp.Recording:
Removed per the speaker's request
Virtual Winter School on Computational Chemistry