Implicit Generative Property Enhancer
Pedro O. Pinheiro, Pan Kessel, Aya Ismail, Sai Pooja Mahajan, Kyunghyun Cho, Saeed Saremi, Nataša Tagasovska
Generative modeling is increasingly important for data-driven computational design problems. Conventional approaches typically pair a generative model with a discriminative model to select or guide samples towards optimized designs. However, these discriminative models often struggle when data is scarce, a common scenario in scientific applications, and are unreliable at the tail-ends of distributions where optimal designs usually lie.We introduce generative property enhancer (GPE), an approach that implicitly guides generation by matching samples with lower property values to high-value ones. Our framework, formulated as a conditional density estimation problem, inherently defines a target distribution with improved properties, compelling the generative model to produce enhanced and diverse designs without relying on auxiliary predictors. GPE offers a simple, scalable, and end-to-end approach for design optimization. It is modality-agnostic and can be seamless integrated across diverse generative architectures and losses.We show our model achieves competitive empirical results in standard protein fitness optimization _in silico_ benchmarks. Finally, we propose iteratively training on a combination of limited real data and self-generated synthetic data, which we show enables extrapolation beyond the original property ranges.