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Fréchet ChemNet Distance: A Metric for Generative Models for Molecules in Drug Discovery
Open accessAbstract
The new wave of successful generative models in machine learning has increased the interest in deep learning driven de novo drug design. However, method comparison is difficult because of various flaws of the currently employed evaluation metrics. We propose an evaluation metric for generative models called Fréchet ChemNet distance (FCD). The advantage of the FCD over previous metrics is that it can detect whether generated molecules are diverse and have similar chemical and biological properties as real molecules.
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“Abbreviations ADAM: Adaptative moment estimation; DRD2: Dopamine receptor D2; FEP: Free energy perturbation; FCD: Fréchet chemnet distance; GPCR: G protein-coupled receptor; QED: Quantitative estimate of drug-likeness; QSAR: Quantitative structure–activity relationship; SAscore: Synthetic accessibility score; SELFIES:…”
BackgroundResultBrown, A. J., Snapkov, I., Akbar, R., Pavlović, M., Miho, E., Sandve, G. K., & Greiff, V. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00071b · Cited by 59
GenSMILES: An enhanced validity conscious representation for inverse design of molecules
Bhadwal, A. S., Kumar, K., & Kumar, N. (2023) · Knowledge-Based Systems · doi:10.1016/j.knosys.2023.110429 · Cited by 17
PCF-VAE: posterior collapse free variational autoencoder for de novo drug design
Bhadwal, A. S., Kumari, M., & Kumar, A. (2025) · Scientific Reports · doi:10.1038/s41598-025-14285-5 · Cited by 7
Metadata
- Authors
- Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., & Klambauer, G.
- Venue
- Journal of Chemical Information and Modeling
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-08-17
- Volume
- 58
- Issue
- 9
- Pages
- 1736-1741
- ISSN
- 1549-9596 (ISSN-L)
- 1549-960X
- OpenAlex
- W2887447356
- Cited by
- 343
- References
- 5
- FWCI
- 18.11
- Type
- journal article
- Language
- en
- Open access
- green
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