ArticleMolecules (Basel, Switzerland)2022
Prediction of Compound Synthesis Accessibility Based on Reaction Knowledge Graph.
Article in Molecules (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Bridging data and discovery: a survey on knowledge graphs in AI for science.National science review · 2026Review
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- RetroScore: graph edit distance-guided retrosynthesis for accessibility scoring with route metrics.Journal of cheminformatics · 2025Article
- A review on knowledge graphs for healthcare: Resources, applications, and promises.Journal of biomedical informatics · 2025Review
- Knowledge Graph and Large Language Model Co-learning via Structure-oriented Retrieval Augmented Generation.Bulletin of the Technical Committee on Data Engineering · 2024Article
- Artificial Intelligence Methods and Models for Retro-Biosynthesis: A Scoping Review.ACS synthetic biology · 2024Article
- Critical assessment of synthetic accessibility scores in computer-assisted synthesis planning.Journal of cheminformatics · 2023Article
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Authors and funding
2 authors.
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Abstract
With the increasing application of deep-learning-based generative models for de novo molecule design, the quantitative estimation of molecular synthetic accessibility (SA) has become a crucial factor for prioritizing the structures generated from generative models. It is also useful for helping in the prioritization of hit/lead compounds and guiding retrosynthesis analysis. In this study, based on the USPTO and Pistachio reaction datasets, a chemical reaction network was constructed for the identification of the shortest reaction paths (SRP) needed to synthesize compounds, and different SRP cut-offs were then used as the threshold to distinguish a organic compound as either an easy-to-synthesize (ES) or hard-to-synthesize (HS) class. Two synthesis accessibility models (DNN-ECFP model and graph-based CMPNN model) were built using deep learning/machine learning algorithms. Compared to other existing synthesis accessibility scoring schemes, such as SYBA, SCScore, and SAScore, our results show that CMPNN (ROC AUC: 0.791) performs better than SYBA (ROC AUC: 0.76), albeit marginally, and outperforms SAScore and SCScore. Our prediction models based on historical reaction knowledge could be a potential tool for estimating molecule SA.
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Registered trials
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