Session 2

We are developing and applying data-driven methodologies to study, synthesize, and utilize chiral molecular structures, with a particular focus on advancing asymmetric catalysis. In this talk, I will highlight recent efforts to address key challenges in predicting and generalizing the behavior of chiral catalysts across diverse reaction spaces.

I will first present a framework for quantifying catalyst generality by evaluating both the selectivity and the breadth of performance across chemical space. I will then show how this approach can be used to uncover underexplored yet broadly applicable chiral catalysts. By correcting for reporting bias in the workflow, I identify catalysts with high generality that may otherwise be overlooked in traditional discovery efforts.

Finally, I will demonstrate the tradeoffs in data representation for enantioselectivity prediction. Using a local chemical space-aware algorithm, I highlight the importance of rational dataset design in building efficient and accurate predictive models.

Together, these studies illustrate how the integration of statistical modeling, mechanistic insight, and chemical intuition enables a more strategic and scalable approach to chiral catalyst development and application.

Jolene Reid is an Assistant Professor at the University of British Columbia, where she leads a research group focused on catalysis, cheminformatics, and machine learning for reaction prediction, catalyst screening, and structure optimization.

She earned her Ph.D. from the University of Cambridge under the supervision of Professor Jonathan Goodman, integrating experiments and computations to study organocatalysis. She then joined the University of Utah as a Marie Skłodowska-Curie Fellow with Professor Matthew Sigman, focusing on statistical modeling of organic molecules and chemical reactions.

Dr. Reid has received prestigious awards, including the Amgen Young Investigator Award (2024) and has published over 40 papers in leading journals.

Applying machine learning algorithms to chemical problems remains challenging, especially in sparse data regimes where there is insufficient data to train complex neural networks. For most chemical reaction optimizations, the amount of data is limited and difficult to expand. Bayesian Optimization (BO) has become a workhorse technique for chemical reaction optimization (as it is well suited to this kind of problem). It has been shown to work exceptionally well with numeric parameters such as temperature, concentration, and reaction time. However, incorporating chemical structure of catalysts into a BO algorithm remains a more challenging task. I will present a new workflow which aims to combine chemical parameterization with standard BO algorithms to optimize a hindered Buchwald C-N coupling reaction. Methods for combining multiple classes of catalysts will be presented, as well as methods for multi-objective optimization capable of navigating search spaces in excess of 2 million potential reaction condition/catalyst pairs.

Dr. Richard Walroth recieved his undergraduate degree from the University of Florida where he worked with Prof. Lisa McElwee-White on multimetallic catalysts. He went on to receive his PhD from Cornell University, where he worked in the lab of Prof. Kyle Lancaster on bioinorganic chemistry and spectroscopy. Following his PhD studies, he worked as a postdoc at NASA where he used machine learning to automate data processing on space craft instruments. From there he went on to SLAC National Lab to work on applying machine learning techniques to powder X-ray diffraction interpretation. Finally, he joined Genentech in 2021 as a Senior Scientist working on using machine learning to optimize catalytic transformations.

Artificial Intelligence is transforming how we approach chemical research and synthesis. By teaching language models to understand and generate the language of chemistry, we have developed complementary AI systems that bridge the gap between computational design and experimental reality. Our large language model system, ChemCrow, represents one of the first demonstrations of an AI system directly controlling robotic synthesis platforms, successfully executing the synthesis of compounds including organocatalysts and chromophores. Complementing this, our small language model system, Saturn, currently the most sample-efficient molecular design algorithm, enables precise molecular generation with built-in synthesizability constraints. Saturn’s innovations include direct optimization against retrosynthetic predictions and integration of building block availability, ensuring that generated molecules are practically accessible. Our work demonstrates how different scales of language models can work together to transform chemical research, from initial molecular design through to physical synthesis, potentially revolutionizing drug discovery, catalysis, and materials development.

Philippe Schwaller joined EPFL as a tenure-track assistant professor in the Institute of Chemical Sciences and Engineering in February 2022. He leads the Laboratory of Artificial Chemical Intelligence, which works on AI-accelerated discovery and synthesis of molecules and materials. Philippe is a core PI of the NCCR Catalysis, a Swiss centre for sustainable chemistry research, education, and innovation, and a co-lead of the foundation models for sciences pillar in the Swiss AI initiative. He belongs to a new generation of scientists with a broad set of skills – in his case, a combination of chemistry, materials science, computer science, and experimental research. Before EPFL, Philippe worked for 5 years at IBM Research and simultaneously completed an MPhil in Physics (University of Cambridge) and a PhD in Chemistry and Molecular Sciences (University of Bern). He also holds a BSc and MSc degree in Materials Science and Engineering (EPFL).

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