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DTSTART;TZID=UTC:20260617T140000
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SUMMARY:AIchemy’s Monthly Webinar Series – June 2026
DESCRIPTION:KEY DETAILS\n\n\n\n\nDATE17th June 2026 TIME14:00 – 15:00 COSTFree LOCATIONOnline MS Teams \n\n\n\n\n\nRECORDINGSClick the YouTube links below to watch each session. \n\n\n\n\nCombinatorial explosion: from atom-bond arrangements to exotic diseases Low-cost mechanism-informed features enable transferable enantioselectivity predictions from sparse data \n\n\n\n\n\n\n\n\n\n\n\nWe are delighted to welcome you to our AIchemy Hub’s monthly webinar series. \n\n\n\nThis month’s talks: \n\n\n\nAssoc. Prof. Timothy Cernak – University of Michigan \n\n\n\nTalk Title: Combinatorial explosion: from atom-bond arrangements to exotic diseases \n\n\n\nChemical synthesis and data science are two fields that operate in synergy. Molecules and the routes to synthesize them are easily represented as graphs while automated chemical synthesis strategies allow more and more synthesis data to be captured\, for instance to feed machine learning algorithms. This talk will detail our work in this area focused on a new class of amine-acid cross coupling reactions\, and the computer-assisted synthesis of drugs and natural products. We have been exploring the breadth of all reactions that could exist\, navigating combinatorial explosions of virtual and plausible reaction methods\, routes to complex molecules\, and the interconnectedness of reaction conditions\, transformations\, and biological functions. \n\n\n\nOur agnostic view of reactions and their mechanisms has recently extended to diseases\, with a focus on One Health. We aspire to produce medicines and treatments for health challenges in endangered species. We call this new area conservation chemistry\, and examples from the frontlines of this field and lab-based research will be shared. \n\n\n\nDr. Simone Gallarati – University of Utah \n\n\n\nTalk Title: Low-cost mechanism-informed features enable transferable enantioselectivity predictions from sparse data \n\n\n\nIn order to optimize an asymmetric reaction\, machine learning (ML) models are frequently implemented to screen virtual libraries of chiral catalysts and identify candidates with superior performance. Unfortunately\, such models are often poorly transferable to new reactions involving a different combination of known substrate types or an entirely unfamiliar class of compounds. In this talk\, I will first introduce a descriptor generation strategy that accounts for possible changes in a reaction’s stereodetermining step with catalyst or substrate identity\, allowing us to model mechanistically complex transformations involving distinct ligand and substrate types. Our ML workflow has led to the optimization of poorly performing examples reported in a substrate scope and to accurate out-of-sample predictions on unseen ligand and reaction partners.1 \n\n\n\nOne limitation of inference-based ML models is the need for large virtual libraries of potential catalysts\, whose curation is frequently associated with significant computational costs. In the second part of the talk\, I will introduce a genetic algorithm-based pipeline2 whereby only a small population of ligands is computed and evaluated experimentally at each iteration of the optimization loop. This strategy leverages the modularity of catalyst scaffolds and is compatible with early reaction optimization campaigns\, requiring the featurization and synthesis of only small batches of ligands. Overall\, these workflows enable streamlined reaction development\, quantitatively transferring knowledge learned on sparse data sets to novel chemical spaces. \n\n\n\nReferences \n\n\n\n(1) Gallarati\, S.; Bucci\, E. M.; Doyle\, A. G.; Sigman\, M. S. Transferable Enantioselectivity Models from Sparse Data. Nature 2026\, 651\, 637–646. \n\n\n\n(2) Gallarati\, S.; van Gerwen\, P.; Schoepfer\, A. A.; Laplaza\, R.; Corminboeuf\, C. Genetic Algorithms for the Discovery of Homogeneous Catalysts. CHIMIA 2023\, 77 (1/2)\, 39.Following the presentations\, there will be time for questions from the audience. \n\n\n\n\n\nSpeakers\n\n\n\n\n\nAssoc. Prof. Timonthy CernakMedicinal Chemistry\n\n\n\n\n\nDr.Simone Gallarati Postdoctoral researcher\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nSpeaker Nominations\n\n\n\nWe welcome suggestions from the community for both our main speaker talks and Early Career Researcher talks (ECR – defined as late-stage PhD or postdocs). The aim of these webinars is to cover a range of topics in digital chemistry\, including general purpose robotic systems\, high-throughput automation\, closed-loop and human-in-the-loop workflows\, generative AI\, multi-fidelity AI\, reinforcement learning\, and optimisation (this is not an exhaustive list).Please fill out the form below to suggest or nominate potential speakers. Self-nominations are also encouraged. \n\n\n\nNominate a speaker
URL:https://aichemy.ac.uk/event/aichemys-monthly-webinar-series-june-2026-2/
CATEGORIES:Webinar
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