Property-Guided Generative Modelling for Robust Model-Based Design with Imbalanced Data

The problem of designing protein sequences with desired properties is challenging, as it requires to explore a high-dimensional protein sequence space with extremely sparse meaningful regions. This has led to the development of model-based optimization (MBO) techniques that aid in the design, by using effective search models guided by the properties over the sequence space. However, the intrinsic imbalanced nature of experimentally derived datasets causes existing MBO approaches to struggle or outright fail. We propose a property-guided variational auto-encoder (PGVAE) whose latent space is explicitly structured by the property values such that samples are prioritized according to these properties. Through extensive benchmarking on real and semi-synthetic protein datasets, we demonstrate that MBO with PGVAE robustly finds sequences with improved properties despite significant dataset imbalances. We further showcase the generality of our approach to continuous design spaces, and its robustness to dataset imbalance in an application to physics-informed neural networks.