ArticleJournal of chemical information and modeling2022
AI-Driven Synthetic Route Design Incorporated with Retrosynthesis Knowledge.
Article in Journal of chemical information and modeling, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- Enhancing biocatalytic retrosynthesis with a graph-to-graph model.Chemical science · 2026Article
- AI for Accelerated Materials Discovery: From Generative Design to Autonomous Realization.Chemical reviews · 2026Article
- AI-driven computational drug design: tools, workflow and challenges.RSC advances · 2026Review
- Organic Chemistry as a Catalyst for AI Innovation: Challenges, Methods, and Emerging Paradigms.Chemical reviews · 2026Review
- Dual-Module Near-Infrared Fluorophores Discovery System via Knowledge Transfer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Molecular Design with Artificial Intelligence: Progress and Perspectives for Small Molecules.Chemical reviews · 2026Review
- Carboxamide: A Privileged Pharmacophore for the Development of Anti-infectious and Anti-cancer Drugs.Current topics in medicinal chemistry · 2026Review
- Cross-disciplinary perspectives on the potential for artificial intelligence across chemistry.Chemical Society reviews · 2025Review
- Local reaction condition optimization via machine learning.Journal of molecular modeling · 2025Review
- Improving route development using convergent retrosynthesis planning.Journal of cheminformatics · 2025Article
- Review
- A data-driven group retrosynthesis planning model inspired by neurosymbolic programming.Nature communications · 2025Article
- Retrosynthesis prediction with an iterative string editing model.Nature communications · 2024Article
- AiZynthFinder 4.0: developments based on learnings from 3 years of industrial application.Journal of cheminformatics · 2024Article
- MISATO: machine learning dataset of protein-ligand complexes for structure-based drug discovery.Nature computational science · 2024Article
- Valence-isomer selective cycloaddition reaction of cycloheptatrienes-norcaradienes.Nature communications · 2024Article
- Recent Developments and Applications of Biocatalytic and Chemoenzymatic Synthesis for the Generation of Diverse Classes of Drugs.Current pharmaceutical biotechnology · 2024Article
- Exploring the ability of machine learning-based virtual screening models to identify the functional groups responsible for binding.Journal of cheminformatics · 2023Article
- Open-Source Machine Learning in Computational Chemistry.Journal of chemical information and modeling · 2023Review
- Predictive chemistry: machine learning for reaction deployment, reaction development, and reaction discovery.Chemical science · 2023Review
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called "ReTReK" that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, indicating that the synthetic routes searched with the knowledge were preferred to those without the knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into a data-driven CASP application is expected to enhance the performance of both existing data-driven CASP applications and those under development.
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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.