ArticleBiophysical journal2025
Transformer graph variational autoencoder for generative molecular design.
Article in Biophysical journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Revolutionizing drug discovery from natural products: The roles of artificial intelligence and multi-omics in accelerating innovation.Acta pharmaceutica Sinica. B · 2026Article
- Artificial intelligence in drug discovery from advanced molecular representation to pipeline applications.Frontiers in bioinformatics · 2026Review
- Integrating artificial intelligence into small molecule development for precision cancer immunomodulation therapy.npj drug discovery · 2025Review
- PCF-VAE: posterior collapse free variational autoencoder for de novo drug design.Scientific reports · 2025Article
- Generative Deep Learning for de Novo Drug Design─A Chemical Space Odyssey.Journal of chemical information and modeling · 2025Review
- Review
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the field of drug discovery, the generation of new molecules with desirable properties remains a critical challenge. Traditional methods often rely on simplified molecular input line entry system representations for molecular input data, which can limit the diversity and novelty of generated molecules. To address this, we present the transformer graph variational autoencoder (TGVAE), an innovative AI model that employs molecular graphs as input data, thus capturing the complex structural relationships within molecules more effectively than string models. To enhance molecular generation capabilities, TGVAE combines a transformer, graph neural network (GNN), and VAE. Additionally, we address common issues like over-smoothing in training GNNs and posterior collapse in VAEs to ensure robust training and improve the generation of chemically valid and diverse molecular structures. Our results demonstrate that TGVAE outperforms existing approaches, generating a larger collection of diverse molecules and discovering structures that were previously unexplored. This advancement not only brings more possibilities for drug discovery but also sets a new level for the use of AI in molecular generation.
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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.