druGAN: An Advanced Generative Adversarial Autoencoder Model for de Novo Generation of New Molecules with Desired Molecular Properties in Silico
Abstract
Deep generative adversarial networks (GANs) are the emerging technology in drug discovery and biomarker development. In our recent work, we demonstrated a proof-of-concept of implementing deep generative adversarial autoencoder (AAE) to identify new molecular fingerprints with predefined anticancer properties. Another popular generative model is the variational autoencoder (VAE), which is based on deep neural architectures. In this work, we developed an advanced AAE model for molecular feature extraction problems, and demonstrated its advantages compared to VAE in terms of (a) adjustability in generating molecular fingerprints; (b) capacity of processing very large molecular data sets; and (c) efficiency in unsupervised pretraining for regression model. Our results suggest that the proposed AAE model significantly enhances the capacity and efficiency of development of the new molecules with specific anticancer properties using the deep generative models.
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druGAN used adversarial autoencoders to generate new molecular fingerprints and drug designs incorporating features such as solubility and absorption based on predefined anticancer drug properties; results suggest substantial improvement in efficiency in generating new drug designs with specific properties.
“As an example, the program druGAN used adversarial autoencoders to generate new molecular fingerprints and drug designs incorporating features such as solubility and absorption based on predefined anticancer drug properties. These results suggest a substantial improvement in the efficiency in generating new drug designs with specific properties [21].”
From The rise of artificial intelligence in healthcare applications (2020) · cites this paper as [21]
An enhanced GAN architecture that included additional molecular parameters such as solubility enabled generation of more chemically diverse molecules and showed improvement in training and generation processes.
“authors proposed an enhanced architecture that also included additional molecular parameters such as solubility and enabled the generation of more chemically diverse molecules [42]. New model clearly showed the improvement in the training and generation processes”
From Converging blockchain and next-generation artificial intelligence technologies to decentralize and accelerate biomedical research and healthcare (2018) · cites this paper as [42]
DNNs have been used to create novel features/descriptors from molecular structure as an alternative to traditional molecular descriptors.
“DNNs have also been used to create novel features / descriptors from their molecular structure as an alternative to traditional molecular descriptors33.”
From Exploiting machine learning for end-to-end drug discovery and development (2019) · cites this paper as [33]
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“Comparing the molecules produced by VAE and AAE, Kadurin et al. 14 also explores the application of NLP models for drug design.”
- Authors
- Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A., & Zhavoronkov, A.
- Affiliations
- Johns Hopkins University (US)
- National Research University Higher School of Economics (RU)
- Corresponding author
- Kadurin, Artur
- Zhavoronkov, Alex
- Venue
- Molecular Pharmaceutics
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-08-04
- Volume
- 14
- Issue
- 9
- Pages
- 3098-3104
- ISSN
- 1543-8384 (ISSN-L)
- 1543-8392
- OpenAlex
- W2736137960
- Cited by
- 587
- References
- 19
- Citations by year
- 2026: 43 · 2025: 67 · 2024: 71 · 2023: 80 · 2022: 59 · 2021: 96 · 2020: 67 · 2019: 57 · 2018: 41 · 2017: 6
- FWCI
- 35.07
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Molecular Pharmaceutics (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Government of the Russian Federation (14.Z50.31.0030)
- 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
- de novo molecule generation, molecular fingerprints, drug discovery, unsupervised pre-training, regression models
- MeSH terms
- Artificial Intelligence, Computer Simulation, Concept Formation, Learning, Models, Theoretical, Neural Networks, Computer
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