A paper from the Laboratory of Data and Chemical Engineering (Professor KANEKO Hiromasa) graced the cover of Volume 66, Issue 12 (2026) of the international academic journal "
Journal of Chemical Information and Modeling."
The aim of this study is to efficiently generate chemical structures near the AD boundary. Among the chemical structures generated using a generative adversarial network (GAN), the chemical structures close to the AD boundary were swapped with the chemical structures in the GAN training data that were farthest from the AD boundary, and the GAN was retrained. Validation using three physical property data sets (the boiling point, melting point, and water solubility) confirmed that the proposed method generates a higher proportion of chemical structures near the AD boundary than conventional GANs.
Supplementary Cover Art:
Journal of Chemical Information and Modeling
Article:
Generation of Molecules Near the Applicability Domain Boundaries of Property Prediction Models
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Japanese version≫
