Robust Model-Based Optimization for Challenging Fitness Landscapes

• URL: http://arxiv.org/abs/2305.13650v3
• Date: Fri, 28 Jun 2024 03:33:28 GMT
• Title: Robust Model-Based Optimization for Challenging Fitness Landscapes
• Authors: Saba Ghaffari, Ehsan Saleh, Alexander G. Schwing, Yu-Xiong Wang, Martin D. Burke, Saurabh Sinha,
• Abstract summary: Protein design involves optimization on a fitness landscape. Leading methods are challenged by sparsity of high-fitness samples in the training set. We show that this problem of "separation" in the design space is a significant bottleneck in existing model-based optimization tools. We propose a new approach that uses a novel VAE as its search model to overcome the problem.
• Score: 96.63655543085258
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Protein design, a grand challenge of the day, involves optimization on a fitness landscape, and leading methods adopt a model-based approach where a model is trained on a training set (protein sequences and fitness) and proposes candidates to explore next. These methods are challenged by sparsity of high-fitness samples in the training set, a problem that has been in the literature. A less recognized but equally important problem stems from the distribution of training samples in the design space: leading methods are not designed for scenarios where the desired optimum is in a region that is not only poorly represented in training data, but also relatively far from the highly represented low-fitness regions. We show that this problem of "separation" in the design space is a significant bottleneck in existing model-based optimization tools and propose a new approach that uses a novel VAE as its search model to overcome the problem. We demonstrate its advantage over prior methods in robustly finding improved samples, regardless of the imbalance and separation between low- and high-fitness samples. Our comprehensive benchmark on real and semi-synthetic protein datasets as well as solution design for physics-informed neural networks, showcases the generality of our approach in discrete and continuous design spaces. Our implementation is available at https://github.com/sabagh1994/PGVAE.

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