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Consensus Prediction of Chemical Reactions with OCHEM-R Platform

  • 2026
  • OriginalPaper
  • Chapter
Published in:
Authors
Igor V. Tetko
Guillaume Godin
Kevin M. Jablonka
Adrian Mirza
Luc Patiny
Published in
Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions
Publisher
Springer Nature Switzerland

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Abstract

This chapter explores the challenges and advancements in predicting chemical reactions, with a focus on multistep retrosynthesis. The OCHEM-R platform is introduced as a solution that combines multiple prediction methods to improve accuracy and confidence in reaction pathways. The platform's consensus modeling approach, which integrates various prediction methods, is detailed, along with its interactive visualization tools that aid chemists in pathway selection. The chapter also discusses the platform's modular structure, allowing for easy integration of new methods and flexible configuration. Additionally, the use of retrieval augmented generation (RAG) to contextualize reaction predictions with literature data is highlighted. The results section presents examples of resolved pathways, demonstrating the platform's effectiveness in predicting chemical reactions. The discussion outlines potential improvements and future directions for the OCHEM-R platform, emphasizing its potential to revolutionize chemical reaction prediction.
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previous chapter MolEncoder: Improved Masked Language Modeling for Molecules
next chapter HeNCler: Node Clustering in Heterophilous Graphs via Learned Asymmetric Similarity
Title
Consensus Prediction of Chemical Reactions with OCHEM-R Platform
Authors
Igor V. Tetko
Guillaume Godin
Kevin M. Jablonka
Adrian Mirza
Luc Patiny
Copyright Year
2026
Book
Artificial Neural Networks and Machine Learning. ICANN 2025 International Workshops and Special Sessions

Print ISBN: 978-3-032-04551-5

Electronic ISBN: 978-3-032-04552-2

Copyright Year: 2026

DOI
https://doi.org/10.1007/978-3-032-04552-2_7
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