Bidirectional Generative Adversarial Network. The bidirectional generative adversarial network (BGAN) model is designed to enhance the estimation of probability density function of molecular configurations. The architecture of BGAN model was inspired from the roundtrip density estimator developed by Liu et al. The BGAN model consists of two pairs of generative adversarial networks: one is used to generate pseudo-molecular coordinates and the other to generate pseudo-latent variables. The coordinate variable 𝒙 and latent variable 𝒛 are independent and identically distributed random variables with probability densities 𝒑(𝒛) and 𝒑(𝒙) in space 𝑿 and 𝒁, respectively. In the coordinate space 𝑿, the generator 𝑮 𝒁→𝑿 takes latent variable 𝒛 as input and attempts to generate pseudo-molecular coordinates 𝒙′ that involve an indistinguishable statistical distribution from that of the training set coordinates 𝒙. The discriminator 𝑫 𝑿 is a binary classifier trained to maximally identify the difference between 𝒙 and 𝒙′. During the model training, 𝑮 𝒁→𝑿 and 𝑫 𝑿 are formally competing with one another, which cooperatively minimizes the statistical difference between pseudo-and true molecular coordinates. The loss functions for the generator and discriminator in the coordinate space are: