  In this paper, we propose a deep evolutionary learning (DEL) process that
integrates fragment-based deep generative model and multi-objective
evolutionary computation for molecular design. Our approach enables (1)
evolutionary operations in the latent space of the generative model, rather
than the structural space, to generate novel promising molecular structures for
the next evolutionary generation, and (2) generative model fine-tuning using
newly generated high-quality samples. Thus, DEL implements a data-model
co-evolution concept which improves both sample population and generative model
learning. Experiments on two public datasets indicate that sample population
obtained by DEL exhibits improved property distributions, and dominates samples
generated by multi-objective Bayesian optimization algorithms.
