ArticleChemical science2022
Predicting reaction conditions from limited data through active transfer learning.
Article in Chemical science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
What it found
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Who cites it
23 citing papers in PubMed.
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- A 50,688-Reaction Data Set Reveals General Ligands and Mechanistic Diversity in C-N Couplings.Journal of the American Chemical Society · 2026Article
- Data-driven recommendation of agents, temperature, and equivalence ratios for organic synthesis.Chemical science · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- Transfer learning from custom-tailored virtual molecular databases to real-world organic photosensitizers for catalytic activity prediction.Communications chemistry · 2025Article
- Application of the digital annealer unit in optimizing chemical reaction conditions for enhanced production yields.Journal of cheminformatics · 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
- Local reaction condition optimization via machine learning.Journal of molecular modeling · 2025Review
- Transfer learning across different photocatalytic organic reactions.Nature communications · 2025Article
- Designing Target-specific Data Sets for Regioselectivity Predictions on Complex Substrates.Journal of the American Chemical Society · 2025Article
- Recommending reaction conditions with label ranking.Chemical science · 2025Article
- An active representation learning method for reaction yield prediction with small-scale data.Communications chemistry · 2025Article
- Molecular optimization using a conditional transformer for reaction-aware compound exploration with reinforcement learning.Communications chemistry · 2025Article
- BatGPT-Chem: A Foundation Large Model for Chemical Engineering.Research (Washington, D.C.) · 2025Article
- A robust data analytical method to investigate sequence dependence in flow-based peptide synthesis.Reaction chemistry & engineering · 2024Article
- Article
- Data Sharing in Chemistry: Lessons Learned and a Case for Mandating Structured Reaction Data.Journal of chemical information and modeling · 2023Review
- Machine Learning Strategies for Reaction Development: Toward the Low-Data Limit.Journal of chemical information and modeling · 2023Review
- Substrate specific closed-loop optimization of carbohydrate protective group chemistry using Bayesian optimization and transfer learning.Chemical science · 2023Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Transfer and active learning have the potential to accelerate the development of new chemical reactions, using prior data and new experiments to inform models that adapt to the target area of interest. This article shows how specifically tuned machine learning models, based on random forest classifiers, can expand the applicability of Pd-catalyzed cross-coupling reactions to types of nucleophiles unknown to the model. First, model transfer is shown to be effective when reaction mechanisms and substrates are closely related, even when models are trained on relatively small numbers of data points. Then, a model simplification scheme is tested and found to provide comparative predictivity on reactions of new nucleophiles that include unseen reagent combinations. Lastly, for a challenging target where model transfer only provides a modest benefit over random selection, an active transfer learning strategy is introduced to improve model predictions. Simple models, composed of a small number of decision trees with limited depths, are crucial for securing generalizability, interpretability, and performance of active transfer learning.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.