Kang, S., Morrone, J. A., Weber, J. K., & Cornell, W. D. (2022). Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation. Journal of chemical information and modeling, 62(4), 801. https://doi.org/10.1021/acs.jcim.1c01545
Citácia podle Chicago (17th ed.)Kang, Seung-Gu, Joseph A. Morrone, Jeffrey K. Weber, a Wendy D. Cornell. "Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation." Journal of Chemical Information and Modeling 62, no. 4 (2022): 801. https://doi.org/10.1021/acs.jcim.1c01545.
Citácia podľa MLA (8th ed.)Kang, Seung-Gu, et al. "Analysis of Training and Seed Bias in Small Molecules Generated with a Conditional Graph-Based Variational Autoencoder─Insights for Practical AI-Driven Molecule Generation." Journal of Chemical Information and Modeling, vol. 62, no. 4, 2022, p. 801, https://doi.org/10.1021/acs.jcim.1c01545.