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Molecular generative model based on conditional variational autoencoder for de novo molecular design
Open accessAbstract
We propose a molecular generative model based on the conditional variational autoencoder for de novo molecular design. It is specialized to control multiple molecular properties simultaneously by imposing them on a latent space. As a proof of concept, we demonstrate that it can be used to generate drug-like molecules with five target properties. We were also able to adjust a single property without changing the others and to manipulate it beyond the range of the dataset.
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“While the conditional VAE has been previously documented in the literature34, its applicability in drug design is constrained by inherent drawbacks such as sub-optimal performance on standard SMILES representations, the posterior collapse problem, and inefficient exploration of latent space.”
From PCF-VAE: posterior collapse free variational autoencoder for de novo drug design (2025) · cites this paper as [34]
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Metadata
- Authors
- Lim, J., Ryu, S., Kim, J. W., & Kim, W. Y.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Kim, Jin Woo
- Kim, Woo Youn
- Venue
- Journal of Cheminformatics
- Published
- 2018-07-11
- Volume
- 10
- Issue
- 1
- Pages
- 31-31
- ISSN
- 1758-2946 (ISSN-L)
- PMC
- PMC6041224
- OpenAlex
- W2808177712
- Cited by
- 421
- References
- 23
- Citations by year
- 2026: 45 · 2025: 62 · 2024: 67 · 2023: 60 · 2022: 55 · 2021: 62 · 2020: 41 · 2019: 26 · 2018: 1
- FWCI
- 24.31
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Journal of Cheminformatics (publishedVersion, cc-by)
- Europe PMC (PubMed Central) (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- arXiv (Cornell University)
- Figshare (cc-by)
- Funders
- National Research Foundation of Korea (NRF-2017R1E1A1A01078109)
- Topics
- Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Keywords
- conditional variational autoencoder, de novo molecular design, generative models, latent space control, drug-like molecules, generative adversarial networks
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