Cell painting is a high-content morphological assay, with a wide range of applications particularly in the field of pharmacology, contributing significantly to the advancement of biomedical research. The prediction of cell painting from brightfield images facilitates rapid analysis and identification of cellular features across numerous samples. This study aims to generate nuclei cell-painted images from corresponding brightfield images using deep learning methods to extract visual information, thus eliminating the need for expensive equipment and the staining process associated with the cell painting assay. A Conditional Generative Adversarial Network (GAN) with Wasserstein loss and Gradient Penalty (GP) is utilized, comprising a U-Net generator along with a ResNet attention input, and a patch discriminator. The brightfield images are fed as input to the ResNet. The output of the ResNet, along with the brightfield images, is then fed into the U-Net model to generate the nuclei cell painting. The evaluation of the predicted images reveals promising results, with a Mean Absolute Error (MAE) value of 0.21, Mean Square Error (MSE) value of 0.34, Structural Similarity Index Measure (SSIM) value of 0.66, and a Peak Signal to Noise Ratio (PSNR) value of 53.42. These metrics collectively demonstrate a notable enhancement compared to the current industry-standard (Cross-Zamirski et al, Label-free prediction of cell painting from brightfield images, 2022). Thus, the framework comprising a Residual WGAN architecture is proposed for the generation of cell-painted nuclei images from brightfield microscopy.