ArticleChemical science2023
Reagent prediction with a molecular transformer improves reaction data quality.
Article in Chemical science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- A general-purpose framework for chemical reaction representation with atomic correspondence and flexible condition adaptation.Journal of cheminformatics · 2026Article
- A Comparative Review of Artificial Intelligence Applications in Small Molecule Versus Peptide Drug Discovery.International journal of molecular sciences · 2026Review
- Article
- 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
- Challenging Reaction Prediction Models to Generalize to Novel Chemistry.ACS central science · 2025Article
- LlaSMol: Advancing Large Language Models for Chemistry with a Large-Scale, Comprehensive, High-Quality Instruction Tuning Dataset.... Conference on Language Modeling · 2024Article
- Developing BioNavi for Hybrid Retrosynthesis Planning.JACS Au · 2024Article
- Application of Transformers in Cheminformatics.Journal of chemical information and modeling · 2024Review
- ORDerly: Data Sets and Benchmarks for Chemical Reaction Data.Journal of chemical information and modeling · 2024Article
- Machine Learning Empowering Drug Discovery: Applications, Opportunities and Challenges.Molecules (Basel, Switzerland) · 2024Review
- Enhancing chemical synthesis: a two-stage deep neural network for predicting feasible reaction conditions.Journal of cheminformatics · 2024Article
- Machine learning-guided strategies for reaction conditions design and optimization.Beilstein journal of organic chemistry · 2024Review
- Enhancing Generic Reaction Yield Prediction through Reaction Condition-Based Contrastive Learning.Research (Washington, D.C.) · 2024Article
- Multistep retrosynthesis combining a disconnection aware triple transformer loop with a route penalty score guided tree search.Chemical science · 2023Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Automated synthesis planning is key for efficient generative chemistry. Since reactions of given reactants may yield different products depending on conditions such as the chemical context imposed by specific reagents, computer-aided synthesis planning should benefit from recommendations of reaction conditions. Traditional synthesis planning software, however, typically proposes reactions without specifying such conditions, relying on human organic chemists who know the conditions to carry out suggested reactions. In particular, reagent prediction for arbitrary reactions, a crucial aspect of condition recommendation, has been largely overlooked in cheminformatics until recently. Here we employ the Molecular Transformer, a state-of-the-art model for reaction prediction and single-step retrosynthesis, to tackle this problem. We train the model on the US patents dataset (USPTO) and test it on Reaxys to demonstrate its out-of-distribution generalization capabilities. Our reagent prediction model also improves the quality of product prediction: the Molecular Transformer is able to substitute the reagents in the noisy USPTO data with reagents that enable product prediction models to outperform those trained on plain USPTO. This makes it possible to improve upon the state-of-the-art in reaction product prediction on the USPTO MIT benchmark.
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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.