  In recent years, deep generative models have been successfully adopted for
various molecular design tasks, particularly in the life and material sciences.
A critical challenge for pre-trained generative molecular design (GMD) models
is to fine-tune them to be better suited for downstream design tasks aimed at
optimizing specific molecular properties. However, redesigning and training an
existing effective generative model from scratch for each new design task is
impractical. Furthermore, the black-box nature of typical downstream
tasks$\unicode{x2013}$such as property prediction$\unicode{x2013}$makes it
nontrivial to optimize the generative model in a task-specific manner. In this
work, we propose a novel approach for a model uncertainty-guided fine-tuning of
a pre-trained variational autoencoder (VAE)-based GMD model through performance
feedback in an active learning setting. The main idea is to quantify model
uncertainty in the generative model, which is made efficient by working within
a low-dimensional active subspace of the high-dimensional VAE parameters
explaining most of the variability in the model's output. The inclusion of
model uncertainty expands the space of viable molecules through decoder
diversity. We then explore the resulting model uncertainty class via black-box
optimization made tractable by low-dimensionality of the active subspace. This
enables us to identify and leverage a diverse set of high-performing models to
generate enhanced molecules. Empirical results across six target molecular
properties, using multiple VAE-based generative models, demonstrate that our
uncertainty-guided fine-tuning approach consistently outperforms the original
pre-trained models.
