  Across scientific domains, generating new models or optimizing existing ones
while meeting specific criteria is crucial. Traditional machine learning
frameworks for guided design use a generative model and a surrogate model
(discriminator), requiring large datasets. However, real-world scientific
applications often have limited data and complex landscapes, making data-hungry
models inefficient or impractical. We propose a new framework, PropEn, inspired
by ``matching'', which enables implicit guidance without training a
discriminator. By matching each sample with a similar one that has a better
property value, we create a larger training dataset that inherently indicates
the direction of improvement. Matching, combined with an encoder-decoder
architecture, forms a domain-agnostic generative framework for property
enhancement. We show that training with a matched dataset approximates the
gradient of the property of interest while remaining within the data
distribution, allowing efficient design optimization. Extensive evaluations in
toy problems and scientific applications, such as therapeutic protein design
and airfoil optimization, demonstrate PropEn's advantages over common
baselines. Notably, the protein design results are validated with wet lab
experiments, confirming the competitiveness and effectiveness of our approach.
