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Implicitly Guided Design with PropEn: Match your Data to Follow the Gradient
Matching, combined with an encoder-decoder architecture, forms a domain-agnostic generative framework for property enhancement.
A Pareto-optimal compositional energy-based model for sampling and optimization of protein sequences
Deep generative models have emerged as a popular machine learning-based approach for inverse design problems in the life sciences.
Multi-segment preserving sampling for deep manifold sampler
We introduce an alternative approach to this guided sampling procedure, multi-segment preserving sampling, that enables the direct inclusion of domain-specific knowledge by designating preserved and non-preserved segments along the input sequence, thereby restricting variation to only select regions.
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