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.

We propose REACTCLASS that automatically maps the low-level concrete chemical entities into the high-level reactant groups without human effort for training data annotation. REACTCLASS is designed to take two special characteristics of the chemical molecules into consideration. The first characteristic is that each chemical molecule can be represented in two modalities: a chemical name in the text and a molecular structure in the graph. We propose to use cross-modal supervision to automatically create the training data for chemical name classification in the text via molecular structure matching in the graph. The second characteristic is that there is a knowledge-aware subword correlation between the surface names of the chemical entities to be classified and that of the reactant groups as class labels. We propose to train a classification model based on the subword cross-attention map between each chemical name and the corresponding reaction group. Experiments demonstrate that REACTCLASS is highly effective, achieving state-of-the-art performance in classifying the chemical names into human-defined reactant groups without requiring human effort for training data annotation.