ArticleJournal of chemical information and modeling2024
ORDerly: Data Sets and Benchmarks for Chemical Reaction Data.
Article in Journal of chemical information and modeling, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Evaluation of Machine Learning Models for Condition Optimization in Diverse Amide Coupling Reactions.ACS omega · 2026Article
- Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions.Molecules (Basel, Switzerland) · 2025Article
- The consolidation of open-source computer-assisted chemical synthesis data into a comprehensive database.Journal of cheminformatics · 2025Article
- Predicting reaction conditions: a data-driven perspective.Chemical science · 2025Review
- ReactionT5: a pre-trained transformer model for accurate chemical reaction prediction with limited data.Journal of cheminformatics · 2025Article
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Carbohydrate Synthesis is Entering the Data-Driven Digital Era.Chemistry (Weinheim an der Bergstrasse, Germany) · 2025Review
- Challenging Reaction Prediction Models to Generalize to Novel Chemistry.ACS central science · 2025Article
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
- Catalysing (organo-)catalysis: Trends in the application of machine learning to enantioselective organocatalysis.Beilstein journal of organic chemistry · 2024Review
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Authors and funding
5 authors.
Funding
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Abstract
Machine learning has the potential to provide tremendous value to life sciences by providing models that aid in the discovery of new molecules and reduce the time for new products to come to market. Chemical reactions play a significant role in these fields, but there is a lack of high-quality open-source chemical reaction data sets for training machine learning models. Herein, we present ORDerly, an open-source Python package for the customizable and reproducible preparation of reaction data stored in accordance with the increasingly popular Open Reaction Database (ORD) schema. We use ORDerly to clean United States patent data stored in ORD and generate data sets for forward prediction, retrosynthesis, as well as the first benchmark for reaction condition prediction. We train neural networks on data sets generated with ORDerly for condition prediction and show that data sets missing key cleaning steps can lead to silently overinflated performance metrics. Additionally, we train transformers for forward and retrosynthesis prediction and demonstrate how non-patent data can be used to evaluate model generalization. By providing a customizable open-source solution for cleaning and preparing large chemical reaction data, ORDerly is poised to push forward the boundaries of machine learning applications in chemistry.
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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.