ArticleACS central science2023
Feed-Forward Neural Network for Predicting Enantioselectivity of the Asymmetric Negishi Reaction.
Article in ACS central science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Predicting Enantioselectivity of Ruthenium-Catalyzed Ketone Hydrogenation with 3D Structure-Based Deep Learning.Journal of the American Chemical Society · 2026Article
- Humanized Anti-PD-1 Antibodies Generated Using The Conditional Kernel-Elastic Autoencoder.bioRxiv : the preprint server for biology · 2026Article
- Benchmarking molecular representations and machine learning algorithms for asymmetric catalysis: a palladium-catalysed decarboxylative asymmetric allylic alkylation case study.Journal of cheminformatics · 2026Article
- Article
- Data-Driven Prediction of Enantioselectivity for the Sharpless Asymmetric Dihydroxylation: Model Development and Experimental Validation.ACS central science · 2025Article
- Recent Advances in Carbon-Based Catalysts for Heterogeneous Asymmetric Catalysis.Molecules (Basel, Switzerland) · 2025Review
- AI Approaches to Homogeneous Catalysis with Transition Metal Complexes.ACS catalysis · 2025Review
- Transfer Learning-Enabled Ligand Prediction for Ni-Catalyzed Atroposelective Suzuki-Miyaura Cross-Coupling Based on Mechanistic Similarity: Leveraging Pd Knowledge for Ni Discovery.Journal of the American Chemical Society · 2025Article
- Applying statistical modeling strategies to sparse datasets in synthetic chemistry.Science advances · 2025Review
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Authors and funding
12 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Density functional theory (DFT) is a powerful tool to model transition state (TS) energies to predict selectivity in chemical synthesis. However, a successful multistep synthesis campaign must navigate energetically narrow differences in pathways that create some limits to rapid and unambiguous application of DFT to these problems. While powerful data science techniques may provide a complementary approach to overcome this problem, doing so with the relatively small data sets that are widespread in organic synthesis presents a significant challenge. Herein, we show that a small data set can be labeled with features from DFT TS calculations to train a feed-forward neural network for predicting enantioselectivity of a Negishi cross-coupling reaction with
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Registered trials
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