ArticleCommunications chemistry2022
Autonomous design of new chemical reactions using a variational autoencoder.
Article in Communications chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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Who cites it
4 citing papers in PubMed.
- AI-Accelerated Discovery of Electrocatalyst Materials.ACS materials Au · 2026Review
- PCF-VAE: posterior collapse free variational autoencoder for de novo drug design.Scientific reports · 2025Article
- Fuzz Testing Molecular Representation Using Deep Variational Anomaly Generation.Journal of chemical information and modeling · 2025Article
- Into the Unknown: How Computation Can Help Explore Uncharted Material Space.Journal of the American Chemical Society · 2022Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Artificial intelligence based chemistry models are a promising method of exploring chemical reaction design spaces. However, training datasets based on experimental synthesis are typically reported only for the optimal synthesis reactions. This leads to an inherited bias in the model predictions. Therefore, robust datasets that span the entirety of the solution space are necessary to remove inherited bias and permit complete training of the space. In this study, an artificial intelligence model based on a Variational AutoEncoder (VAE) has been developed and investigated to synthetically generate continuous datasets. The approach involves sampling the latent space to generate new chemical reactions. This developed technique is demonstrated by generating over 7,000,000 new reactions from a training dataset containing only 7,000 reactions. The generated reactions include molecular species that are larger and more diverse than the training set.
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Registered trials
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