https://www.selleckchem.com/Androgen-Receptor.html
There has been a surge of interest in applying deep learning (DL) to microstructure generation and materials design. However, existing DL-based methods are generally limited in generating (1) microstructures with high resolution, (2) microstructures with high variability, (3) microstructures with guaranteed periodicity, and (4) highly controllable microstructures. In this study, a DL approach based on a stacked generative adversarial network (StackGAN-v2) is proposed to overcome these shortcomings. The presented modeling approach can r
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