To accelerate entropic path sampling, here we developed a deep learning approach, called bidirectional generative adversarial network -entropic path sampling (BGAN-EPS), by integrating EPS with deep generative models (Figure ). BGAN-EPS enhances the estimation of probability density function (PDF) of molecular configurations by generating pseudo-molecular configurations that are statistically indistinguishable from input molecular configurations sampled from reaction dynamics trajectories. The method was first tested using cyclopentadiene dimerization, and then applied to investigate the entropic profiles for NgnD-catalyzed Diels-Alder reaction in the gas phase. The reaction is the first natural enzyme known to catalyze the [6+4]cycloaddition and also the first enzyme-catalyzed ambimodal cycloaddition where both [6+4] and [4+2] adducts were experimentally identified. Although quasiclassical trajectory (QCT) simulations have been conducted to investigate the ambimodal selectivity of NgnD-catalyzed Diels-Alder reaction, the entropic origin behind the selectivity remains elusive. Besides establishing a new method, our study demonstrates how BGAN-EPS can facilitate chemists to understand the interplay between energy and entropy along post-TS reaction path.