Kjell Jorner

Last Name

Jorner

First Name

Kjell

ORCID

0000-0002-4191-6790

Organisational unit

09781 - Jorner, Kjell / Jorner, Kjell

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Publications 1 - 10 of 11

Stereochemistry-aware string-based molecular generation
Tom , Gary; Yu , Edwin; Yoshikawa , Naruki; et al. (2025)
PNAS Nexus
This study investigates the impact of incorporating stereochemical information, a crucial aspect of computational drug discovery and materials design, in molecular generative modeling. We present a detailed comparison of stereochemistry-aware and conventionally stereochemistry-unaware string-based generative approaches, utilizing both genetic algorithms and reinforcement learning-based techniques. To evaluate these models, we introduce novel benchmarks specifically designed to assess the importance of stereochemistry-aware generative modeling. Our results demonstrate that stereochemistry-aware models generally perform on par with or surpass conventional algorithms across various stereochemistry-sensitive tasks. However, we also observe that in scenarios where stereochemistry plays a less critical role, stereochemistry-aware models may face challenges due to the increased complexity of the chemical space they must navigate. This work provides insights into the trade-offs involved in incorporating stereochemical information in molecular generative models and offers guidance for selecting appropriate approaches based on specific application requirements.
Predicting Reaction Feasibility and Selectivity of Aromatic C─H Thianthrenation with a QM–ML Hybrid Approach
Sigmund , Lukas M.; Seifert, Tina; Halder , Riya; et al. (2025)
Angewandte Chemie. International Edition
The direct thianthrenation of aromatic C─H bonds is a valuable late-stage functionalization strategy that can assist, for example, the development of new drugs. We herein present a predictive computational model for this reaction, denoted PATTCH, which is based on semiempirical quantum mechanics and machine learning. It classifies each Caromatic–H unit either as reactive or not with an accuracy of above 90%. It can address both the site-selectivity and reaction feasibility question associated with the thianthrenation protocol. First, this was achieved by selecting carefully engineered features, which take into account the electronic and steric influence on the site-selectivity. Second, parallel experimentation was used to supplement the available literature data with 54 new negative reactions (unsuccessful thianthrenation), which we show was instrumental for developing the PATTCH tool. Ultimately, we successfully applied the model to a challenging test set encompassing the differentiation between carbocycle versus heterocycle functionalization, the identification of substrates that were reported to result in a mixture of isomeric products, and to molecules that could not be thianthrenated. The computational predictions were experimentally validated. The PATTCH tool can be obtained free of charge from https://github.com/MolecularAI/thianthrenation_prediction.
Predicting Reactivity with Machine Learning
Jacot‐Descombes, Lauriane; Jorner, Kjell (2025)
Artificial Intelligence in Catalysis: Experimental and Computational Methodologies
The integration of machine learning into chemistry has revolutionized the field, enabling experimental and digital chemists to predict and elucidate reactions more efficiently. This chapter focuses on the recent applications of data-driven models to predict various aspects of chemical reactivity, such as yields, activation energies, rate constants, selectivities, and catalytic turnover frequencies. We will provide an overview of diverse predictive methods, from traditional approaches like quantitative structure-activity relationships to modern deep learning techniques, as well as strategies specific to catalysis, such as volcano plots. Despite significant advancements, prominent obstacles remain, among them data quality and model generalizability. We will conclude this review by highlighting these challenges and possible ways to overcome them.
Educating Future Chemists in the Age of AI: A Digital Chemistry Course
Jacot-Descombes, Lauriane; Schmid, Stefan; Jorner, Kjell (2025)
Chimia
Artificial intelligence (AI) and machine learning (ML) are developing fast and are increasingly adopted in both chemical industry and academic research. With the projected role such tools will play in the future, for every chemist, these developments call for a fundamental and sound education for future generations of scientists in these areas. In this perspective, we describe the development of the course Digital Chemistry at ETH Zurich, which addresses these topics. In particular, we outline our approach to teaching ML and its applications in chemistry. We especially emphasize that the skills of understanding, applying and critically assessing ML models will be fundamental for future chemists. We hope that this article will serve as inspiration for educators in this field and help to enhance the teaching in this area of future significance.
Computational tools for the prediction of site- and regioselectivity of organic reactions
Sigmund, Lukas M.; Assante, Michele; Johansson, Magnus J.; et al. (2025)
Chemical Science
The regio- and site-selectivity of organic reactions is one of the most important aspects when it comes to synthesis planning. Due to that, massive research efforts were invested into computational models for regio- and site-selectivity prediction, and the introduction of machine learning to the chemical sciences within the past decade has added a whole new dimension to these endeavors. This review article walks through the currently available predictive tools for regio- and site-selectivity with a particular focus on machine learning models while being organized along the individual reaction classes of organic chemistry. Respective featurization techniques and model architectures are described and compared to each other; applications of the tools to critical real-world examples are highlighted. This paper aims to serve as an overview of the field's status quo for both the intended users of the tools, that is synthetic chemists, as well as for developers to find potential new research avenues.
Chemical education in digital chemistry
Fung, Fun Man; Lederbauer, Magdalena; Choo, Yvonne S.L.; et al. (2024)
Chem
In this digital age where machine learning has won the Nobel Prizes in both Physics and Chemistry, it is ever more important to give chemistry students an educational advantage that will enable them to use the tools of artificial intelligence and machine learning to enhance both their study experience and their future research. In this Voices article, chemistry education and research experts gather to share their implementation and utilization of these data-driven tools in classes and in labs.
Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis
Schmid, Stefan; Schlosser, Leon; Glorius, Frank; et al. (2024)
Beilstein Journal of Organic Chemistry
Organocatalysis has established itself as a third pillar of homogeneous catalysis, besides transition metal catalysis and biocatalysis, as its use for enantioselective reactions has gathered significant interest over the last decades. Concurrent to this development, machine learning (ML) has been increasingly applied in the chemical domain to efficiently uncover hidden patterns in data and accelerate scientific discovery. While the uptake of ML in organocatalysis has been comparably slow, the last two decades have showed an increased interest from the community. This review gives an overview of the work in the field of ML in organocatalysis. The review starts by giving a short primer on ML for experimental chemists, before discussing its application for predicting the selectivity of organocatalytic transformations. Subsequently, we review ML employed for privileged catalysts, before focusing on its application for catalyst and reaction design. Concluding, we give our view on current challenges and future directions for this field, drawing inspiration from the application of ML to other scientific domains.
Inverse Design of Singlet-Fission Materials with Uncertainty-Controlled Genetic Optimization
Schaufelberger, Luca; Blaskovits, J. Terence; Laplaza, Ruben; et al. (2025)
Angewandte Chemie. International Edition
Singlet fission has shown potential for boosting the efficiency of solar cells, but the scarcity of suitable molecular materials hinders its implementation. We introduce an uncertainty-controlled genetic algorithm (ucGA) based on ensemble machine learning predictions from different molecular representations that concurrently optimizes excited state energies, synthesizability, and exciton size for the discovery of singlet fission materials. The ucGA allows us to efficiently explore the chemical space spanned by the reFORMED fragment database, which consists of 45,000 cores and 5,000 substituents derived from crystallographic structures assembled in the FORMED repository. Running the ucGA in an exploitative setup performs local optimization on variations of known singlet fission scaffolds, such as acenes. In an explorative mode, hitherto unknown candidates displaying excellent excited state properties for singlet fission are generated. We suggest a class of heteroatom-rich mesoionic compounds as acceptors for charge-transfer mediated singlet fission. When included in larger donor-acceptor systems, these units exhibit localization of the triplet state, favorable diradicaloid character and suitable triplet energies for exciton injection into semiconductor solar cells.
Putting Chemical Knowledge to Work in Machine Learning for Reactivity
Jorner, Kjell (2023)
Chimia
Machine learning has been used to study chemical reactivity for a long time in fields such as physical organic chemistry, chemometrics and cheminformatics. Recent advances in computer science have resulted in deep neural networks that can learn directly from the molecular structure. Neural networks are a good choice when large amounts of data are available. However, many datasets in chemistry are small, and models utilizing chemical knowledge are required for good performance. Adding chemical knowledge can be achieved either by adding more information about the molecules or by adjusting the model architecture itself. The current method of choice for adding more information is descriptors based on computed quantum-chemical properties. Exciting new research directions show that it is possible to augment deep learning with such descriptors for better per formance in the low-data regime. To modify the models, differentiable programming enables seamless merging of neural networks with mathematical models from chemistry and physics. The resulting methods are also more data-efficient and make better predictions for molecules that are different from the initial dataset on which they were trained. Application of these chemistry-informed machine learning methods promise to accelerate research in fields such as drug design, materials design, catalysis and reactivity.
Ultrafast Computational Screening of Molecules with Inverted Singlet-Triplet Energy Gaps Using the Pariser-Parr-Pople Semiempirical Quantum Chemistry Method
Jorner, Kjell; Pollice, Robert; Lavigne, Cyrille; et al. (2024)
The Journal of Physical Chemistry A
Molecules with an inverted energy gap between their first singlet and triplet excited states have promising applications in the next generation of organic light-emitting diode (OLED) materials. Unfortunately, such molecules are rare, and only a handful of examples are currently known. High-throughput virtual screening could assist in finding novel classes of these molecules, but current efforts are hampered by the high computational cost of the required quantum chemical methods. We present a method based on the semiempirical Pariser-Parr-Pople theory augmented by perturbation theory and show that it reproduces inverted gaps at a fraction of the cost of currently employed excited-state calculations. Our study paves the way for ultrahigh-throughput virtual screening and inverse design to accelerate the discovery and development of this new generation of OLED materials.

Publications 1 - 10 of 11

Kjell Jorner

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