  Molecular design based on generative models, such as variational autoencoders
(VAEs), has become increasingly popular in recent years due to its efficiency
for exploring high-dimensional molecular space to identify molecules with
desired properties. While the efficacy of the initial model strongly depends on
the training data, the sampling efficiency of the model for suggesting novel
molecules with enhanced properties can be further enhanced via latent space
optimization. In this paper, we propose a multi-objective latent space
optimization (LSO) method that can significantly enhance the performance of
generative molecular design (GMD). The proposed method adopts an iterative
weighted retraining approach, where the respective weights of the molecules in
the training data are determined by their Pareto efficiency. We demonstrate
that our multi-objective GMD LSO method can significantly improve the
performance of GMD for jointly optimizing multiple molecular properties.
