  We introduce the Kernel-Elastic Autoencoder (KAE), a self-supervised
generative model based on the transformer architecture with enhanced
performance for molecular design. KAE is formulated based on two novel loss
functions: modified maximum mean discrepancy and weighted reconstruction. KAE
addresses the long-standing challenge of achieving valid generation and
accurate reconstruction at the same time. KAE achieves remarkable diversity in
molecule generation while maintaining near-perfect reconstructions on the
independent testing dataset, surpassing previous molecule-generating models.
KAE enables conditional generation and allows for decoding based on beam search
resulting in state-of-the-art performance in constrained optimizations.
Furthermore, KAE can generate molecules conditional to favorable binding
affinities in docking applications as confirmed by AutoDock Vina and Glide
scores, outperforming all existing candidates from the training dataset. Beyond
molecular design, we anticipate KAE could be applied to solve problems by
generation in a wide range of applications.
