ArticleJournal of chemical information and modeling2020
De Novo Protein Design for Novel Folds Using Guided Conditional Wasserstein Generative Adversarial Networks.
Article in Journal of chemical information and modeling, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- DESIGN: a framework for generative AI-enabled crop molecular design-driven breeding.Science China. Life sciences · 2026Review
- Protein function-conditioned language models for variant effect prediction and controllable design.Biophysical journal · 2026Article
- Multimodal diffusion for joint design of protein sequence and structure.Protein science : a publication of the Protein Society · 2025Article
- The development of the generative adversarial supporting vector machine for molecular property generation.Journal of cheminformatics · 2025Article
- AlphaFold distillation for inverse protein design.Scientific reports · 2025Article
- A review of machine learning methods for imbalanced data challenges in chemistry.Chemical science · 2025Review
- ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.Briefings in bioinformatics · 2025Article
- Leveraging large language models for peptide antibiotic design.Cell reports. Physical science · 2025Article
- Generative β-hairpin design using a residue-based physicochemical property landscape.Biophysical journal · 2024Article
- AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.Molecules (Basel, Switzerland) · 2024Review
- SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.Briefings in bioinformatics · 2024Article
- Interactive design generation and optimization from generative adversarial networks in spatial computing.Scientific reports · 2024Article
- Diffusion models in bioinformatics and computational biology.Nature reviews bioengineering · 2024Article
- Machine Learning Methods in Protein-Protein Docking.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Graph Domain Adaptation via Theory-Grounded Spectral Regularization.... International Conference on Learning Representations · 2023Article
- HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.Bioinformatics (Oxford, England) · 2023Article
- GeoPacker: A novel deep learning framework for protein side-chain modeling.Protein science : a publication of the Protein Society · 2022Article
- Article
- Structural signatures: a web server for exploring a database of and generating protein structural features from human cell lines and tissues.Database : the journal of biological databases and curation · 2022Article
- Conditional generative modeling for de novo protein design with hierarchical functions.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Although massive data is quickly accumulating on protein sequence and structure, there is a small and limited number of protein architectural types (or structural folds). This study is addressing the following question: how well could one reveal underlying sequence-structure relationships and design protein sequences for an arbitrary, potentially novel, structural fold? In response to the question, we have developed novel deep generative models, namely, semisupervised gcWGAN (guided, conditional, Wasserstein Generative Adversarial Networks). To overcome training difficulties and improve design qualities, we build our models on conditional Wasserstein GAN (WGAN) that uses Wasserstein distance in the loss function. Our major contributions include (1) constructing a low-dimensional and generalizable representation of the fold space for the
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.