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Articles by Junren Li
Guiding large language models to predict edit sequences for molecular synthesizability optimization
Abstract Generative artificial intelligence has revolutionized the exploration of chemical space, yet a critical bottleneck remains that a substantial fraction of generated molecules is synthetically inaccessible. Current solutions, such as post hoc filtering or projection-based methods, often compromise structural novelty or disrupt key pharmacophores by forcing molecules into predefined synthetic templates.
Chemist-aligned retrosynthesis by ensembling diverse inductive bias models
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Abstract Chemical synthesis remains a critical bottleneck in the discovery and manufacture of functional small molecules1−3. While AI-assisted synthesis planning has proliferated in recent years, a detailed understanding of its failure modes has not been achieved, and models still struggle with predicting less frequent, yet strategically critical reactions, as well as hallucinated, incorrect predictions misaligned with chemists’ expectations4−12.
By Krzysztof Maziarz, Guoqing Liu, Felix Pultar, John Gardner, Tobias Gensch, Hubert Misztela, Austin Tripp, Junren Li, Aleksei S. Kornev, Piotr Gaiński, Holger Hoefling, Mike E. Fortunato, Andrew Baxter, Darren L. Poole, Jennifer M. Elward, Adrian Krzyzanowski, Peter Pogany, Stephen D. Pickett, Ian Wall, Christopher Bishop, Philip Humphreys, James Lumley, Mario P. Wiesenfeldt, Marwin H. S. Segler, Jean Helie
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Nature
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Single-step retrosynthesis prediction by leveraging commonly preserved substructures - Nature Communications
Abstract Retrosynthesis analysis is an important task in organic chemistry with numerous industrial applications. Previously, machine learning approaches employing natural language processing techniques achieved promising results in this task by first representing reactant molecules as strings and subsequently predicting reactant molecules using text generation or machine translation models.
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