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Quantitative structure‐property relationship models are useful in efficiently searching for molecules with desired properties in drug discovery and materials development. In recent years, many such models based on graph neural networks, showing good prediction performance, have been reported. Training graph neural networks generally require many samples, but by using a training method for a small...
The development of novel organic compounds with desired properties is time consuming and costly. Thus, the quantitative structure‐property relationship (QSPR) model is used widely for efficiently discovering compounds with the desired properties. Novel structures can be generated from a variety of input structures in silico by structure generators. We previously developed the structure generator DAECS...
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