MolMIM is a probabilistic transformer-based encoder-decoder model trained with a Mutual Information Machine (MIM) loss. Contrary to the VAE loss which minimizes the divergence between the latent space distribution and the prior, the MIM loss maximizes the mutual information between an observation and its latent representation. This loss prevents the decoder from ignoring the latent variable in decoding which in turn naturally prevents posterior collapse. Additionally, the loss function in the MolMIM model imposes a low marginal entropy in the latent representation which encourages the formation of a well clustered latent space.