Significant progress has been made in the field of 3D generative models, including variational autoencoders (VAE) , convolution neural network (CNN) , generative adversarial network (GAN) , and graph neural network (GNN) . These advances have enabled the development of deep learning models capable of directly generating de novo molecules in the 3D space. Among these approaches, Pocket2Mol has emerged as a noteworthy innovation, enhancing both efficiency and molecule quality compared to previous structure-based drug design models . More specifically, Pocket2Mol is a novel E(3)-equivariant generative neural network that has been pretrained on the CrossDock dataset. It effectively captures spatial and bonding relationships between atoms within the binding pockets. The conditional molecular sampling algorithm employed by Pocket2Mol demonstrates efficiency in characterizing novel position generation strategies and accurately predicting element types without relying on MCMC (Markov chain Monte Carlo) . Importantly, molecules sampled from Pocket2Mol exhibit significantly improved binding affinity as validated through experimental evaluations .