2021/07/07 by Alvin Chan, Chan, Alvin, Ali Madani +5
Biochemistry, Genetics and Molecular Biology · Computer Science · #Computation and Language (cs.CL) #FOS: Biological sciences #FOS: Computer and information sciences #Machine Learning (cs.LG) #Protein Structure and Dynamics #Quantitative Methods (q-bio.QM) #Software Engineering Research #Topic Modeling
paper · pdf · doi:10.48550/arxiv.2107.02968
openalex publication_date 2021/07/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Attribute extrapolation in sample generation is challenging for deep neural networks operating beyond the training distribution. We formulate a new task for extrapolation in sequence generation, focusing on natural language and proteins, and propose GENhance, a generative framework that enhances attributes through a learned latent space. Trained on movie reviews and a computed protein stability dataset, GENhance can generate strongly-positive text reviews and highly stable protein sequences without being exposed to similar data during training. We release our benchmark tasks and models to contribute to the study of generative modeling extrapolation and data-driven design in biology and chemistry.