Mol-CycleGAN: a generative model for molecular optimization
Open accessAbstract
Designing a molecule with desired properties is one of the biggest challenges in drug development, as it requires optimization of chemical compound structures with respect to many complex properties. To improve the compound design process, we introduce Mol-CycleGAN—a CycleGAN-based model that generates optimized compounds with high structural similarity to the original ones. Namely, given a molecule our model generates a structurally similar one with an optimized value of the considered property. We evaluate the performance of the model on selected optimization objectives related to structural properties (presence of halogen groups, number of aromatic rings) and to a physicochemical property (penalized logP). In the task of optimization of penalized logP of drug-like molecules our model significantly outperforms previous results.
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Generative models have been used to compile focused and diversity-oriented chemical libraries, design inhibitors de novo, and perform hit-to-lead optimization, typically in a multiobjective mode.
“Generative models have been used to compile both focused and diversity-oriented chemical libraries,149,150 to design inhibitors de novo,151 and to perform hit-to-lead optimization,152 typically in a multiobjective mode where e.g. selectivity, synthetic accessibility, and drug-like properties are improved simultaneously.153,154”
From Advancing Targeted Protein Degradation via Multiomics Profiling and Artificial Intelligence (2023) · cites this paper as [152]
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- Authors
- Maziarka, Ł., Pocha, A., Kaczmarczyk, J., Rataj, K., Danel, T., & Warchoł, M.
- Affiliations
- Jagiellonian University (PL)
- City of Prague (CZ)
- Corresponding author
- Maziarka, Łukasz
- Venue
- Journal of Cheminformatics
- Published
- 2020-01-08
- Volume
- 12
- Issue
- 1
- Pages
- 2-2
- ISSN
- 1758-2946 (ISSN-L)
- PMC
- PMC6950853
- OpenAlex
- W2913351693
- Cited by
- 275
- References
- 40
- Citations by year
- 2026: 33 · 2025: 58 · 2024: 47 · 2023: 50 · 2022: 37 · 2021: 41 · 2020: 7 · 2019: 2
- FWCI
- 16.73
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Journal of Cheminformatics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- Homo Politicus (Academy of Humanities and Economics in Lodz) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Keywords
- CycleGAN, generative models, molecular optimization, drug-like molecules, structural similarity, aromatic rings, property optimization, molecular design
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