  Deep generative models have emerged as a popular machine learning-based
approach for inverse design problems in the life sciences. However, these
problems often require sampling new designs that satisfy multiple properties of
interest in addition to learning the data distribution. This multi-objective
optimization becomes more challenging when properties are independent or
orthogonal to each other. In this work, we propose a Pareto-compositional
energy-based model (pcEBM), a framework that uses multiple gradient descent for
sampling new designs that adhere to various constraints in optimizing distinct
properties. We demonstrate its ability to learn non-convex Pareto fronts and
generate sequences that simultaneously satisfy multiple desired properties
across a series of real-world antibody design tasks.
