Molecular generative model based on conditional variational autoencoder for de novo molecular design
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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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Conditional VAE 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.
“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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Unlocking the Future of Drug Development: Generative AI, Digital Twins, and Beyond
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Deep reinforcement learning for inverse inorganic materials design
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Sample efficient reinforcement learning with active learning for molecular design
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Deep Lead Optimization: Leveraging Generative AI for Structural Modification
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AI-Sketcher : A Deep Generative Model for Producing High-Quality Sketches
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pdCSM-cancer: Using Graph-Based Signatures to Identify Small Molecules with Anticancer Properties
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Self-citationDeep learning enabled inorganic material generator
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Computational Approaches for De Novo Drug Design: Past, Present, and Future
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MoleGuLAR: Molecule Generation Using Reinforcement Learning with Alternating Rewards
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Modern machine learning for tackling inverse problems in chemistry: molecular design to realization
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Variational Autoencoders for Jet SimulationPreprint
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Artificial Intelligence Models and Tools for the Assessment of Drug–Herb Interactions
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A review of machine learning-based methods for predicting drug–target interactions
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- 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 · 2017: 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
- Physical Sciences › Computer Science › Computational Theory and Mathematics › 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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