Various neural network architectures have been applied in generative models to directly generate drug-like molecules at 1D sequence, 2D graph or 3D conformation level with or without protein structure, including recurrent neural networks (RNN) , variational autoencoders , generative adversarial networks , 3D convolutional networks (CNNs) , flow-based model and, more recently, diffusion based models etc. Among these models, shape conditioned generative model represents an alternative way for doing ligand based de novo design and in contrast to conventional database screening methods, it can explore a much larger chemical space beyond the databases of known chemicals.