The Mixture Density Network (MDN) is an architecture commonly used in deep learning to model complex probability distributions, which is particularly suitable for handling data with multi-modal distributions, as it introduces the concept of mixture models to flexibly capture multiple modes in the data. The purpose of this module is to effectively select ligand poses and enrich activity by learning the complex probability distribution of protein-ligand interactions. By embedding the encoding nodes of proteins and ligands and processing them through a series of linear, normalization, activation, and Dropout layers, the module ultimately utilizes three linear layers to output the mean, standard deviation, and mixture coefficients.