ArticleJournal of the American Chemical Society2024
Incorporating Synthetic Accessibility in Drug Design: Predicting Reaction Yields of Suzuki Cross-Couplings by Leveraging AbbVie's 15-Year Parallel Library Data Set.
Article in Journal of the American Chemical Society, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Which Reaction Conditions Work on Drug-Like Molecules? Lessons from 66,000 High-Throughput Experiments.ACS central science · 2026Article
- Design and Synthesis of Marine Sarocladione Derivatives with Potential Anticancer Activity.Marine drugs · 2026Article
- Expediting hit-to-lead progression in drug discovery through reaction prediction and multi-dimensional optimization.Nature communications · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- PTML models of self assembled ligand free nanoparticle catalysts for cross coupling reactions.Scientific reports · 2025Article
- Evolving Landscape: Accounts of Academic-Industrial Collaborations in Chemistry Technology.ACS medicinal chemistry letters · 2025Review
- Graph-sequence enhanced transformer for template-free prediction of natural product biosynthesis.Patterns (New York, N.Y.) · 2025Article
- Highly parallel optimisation of chemical reactions through automation and machine intelligence.Nature communications · 2025Article
- Negative chemical data boosts language models in reaction outcome prediction.Science advances · 2025Article
- Developing Pharmaceutically Relevant Pd-Catalyzed C-N Coupling Reactivity Models Leveraging High-Throughput Experimentation.Journal of the American Chemical Society · 2025Article
- Applying Active Learning toward Building a Generalizable Model for Ni-Photoredox Cross-Electrophile Coupling of Aryl and Alkyl Bromides.Journal of the American Chemical Society · 2025Article
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Application of Density Functional Theory to Molecular Engineering of Pharmaceutical Formulations.International journal of molecular sciences · 2025Review
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9 authors.
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Abstract
Despite the increased use of computational tools to supplement medicinal chemists' expertise and intuition in drug design, predicting synthetic yields in medicinal chemistry endeavors remains an unsolved challenge. Existing design workflows could profoundly benefit from reaction yield prediction, as precious material waste could be reduced, and a greater number of relevant compounds could be delivered to advance the design, make, test, analyze (DMTA) cycle. In this work, we detail the evaluation of AbbVie's medicinal chemistry library data set to build machine learning models for the prediction of Suzuki coupling reaction yields. The combination of density functional theory (DFT)-derived features and Morgan fingerprints was identified to perform better than one-hot encoded baseline modeling, furnishing encouraging results. Overall, we observe modest generalization to unseen reactant structures within the 15-year retrospective library data set. Additionally, we compare predictions made by the model to those made by expert medicinal chemists, finding that the model can often predict both reaction success and reaction yields with greater accuracy. Finally, we demonstrate the application of this approach to suggest structurally and electronically similar building blocks to replace those predicted or observed to be unsuccessful prior to or after synthesis, respectively. The yield prediction model was used to select similar monomers predicted to have higher yields, resulting in greater synthesis efficiency of relevant drug-like molecules.
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