ArticleJournal of cheminformatics2022
Improving VAE based molecular representations for compound property prediction.
Article in Journal of cheminformatics, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Blood‒brain barrier permeability assessment: from biomimetic models to multimodal imaging.Journal of Zhejiang University. Science. B · 2026Review
- Deep learning for small-molecule drug discovery: From molecular design to clinical translation.Journal of pharmaceutical analysis · 2026Review
- Task-specific pre-training for molecular property prediction.Briefings in bioinformatics · 2026Article
- Conditional Variational AutoEncoder to Predict Suitable Conditions for Hydrogenation Reactions.Molecules (Basel, Switzerland) · 2025Article
- Diffusion Models at the Drug Discovery Frontier: A Review on Generating Small Molecules Versus Therapeutic Peptides.Biology · 2025Review
- A systematic review of deep learning chemical language models in recent era.Journal of cheminformatics · 2024Review
- Revolutionizing adjuvant development: harnessing AI for next-generation cancer vaccines.Frontiers in immunology · 2024Review
- Data-driven discovery of innate immunomodulatorsChemical science · 2023Article
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Authors and funding
8 authors.
Funding
Abstract
Collecting labeled data for many important tasks in chemoinformatics is time consuming and requires expensive experiments. In recent years, machine learning has been used to learn rich representations of molecules using large scale unlabeled molecular datasets and transfer the knowledge to solve the more challenging tasks with limited datasets. Variational autoencoders are one of the tools that have been proposed to perform the transfer for both chemical property prediction and molecular generation tasks. In this work we propose a simple method to improve chemical property prediction performance of machine learning models by incorporating additional information on correlated molecular descriptors in the representations learned by variational autoencoders. We verify the method on three property prediction tasks. We explore the impact of the number of incorporated descriptors, correlation between the descriptors and the target properties, sizes of the datasets etc. Finally, we show the relation between the performance of property prediction models and the distance between property prediction dataset and the larger unlabeled dataset in the representation space.
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Registered trials
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