ArticleBioinformatics (Oxford, England)2022
Conditional generative modeling for de novo protein design with hierarchical functions.
Article in Bioinformatics (Oxford, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Impact of AI-Assisted Diagnosis on American Patients' Trust in and Intention to Seek Help From Health Care Professionals: Randomized, Web-Based Survey Experiment.Journal of medical Internet research · 2025Trial
- Attribution assignment for deep-generative sequence models enables interpretability analysis using positive-only data.PLoS computational biology · 2026Article
- What Do Generative Models Learn About Adaptive Immune Receptor Repertoires? A Benchmark Study.bioRxiv : the preprint server for biology · 2026Article
- Generative AI for controllable protein sequence design: A survey.npj drug discovery · 2026Review
- De novo functional protein sequence generation: overcoming data scarcity through regeneration and large language models.Briefings in bioinformatics · 2026Article
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- Controllable Generation of Pathogen-Specific Antimicrobial Peptides Through Knowledge-Aware Prompt Diffusion Model.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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- Revolutionizing oncology: the role of Artificial Intelligence (AI) as an antibody design, and optimization tools.Biomarker research · 2025Review
- AI-driven antibody design with generative diffusion models: current insights and future directions.Acta pharmacologica Sinica · 2025Review
- TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.Briefings in bioinformatics · 2024Article
- ProteinReDiff: Complex-based ligand-binding proteins redesign by equivariant diffusion-based generative models.Structural dynamics (Melville, N.Y.) · 2024Article
- Revolutionizing Molecular Design for Innovative Therapeutic Applications through Artificial Intelligence.Molecules (Basel, Switzerland) · 2024Review
- Machine learning-aided design and screening of an emergent protein function in synthetic cells.Nature communications · 2024Article
- New regulatory thinking is needed for AI-based personalised drug and cell therapies in precision oncology.NPJ precision oncology · 2024Review
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Exploring the Protein Sequence Space with Global Generative Models.Cold Spring Harbor perspectives in biology · 2023Review
- Review
- HelixGAN a deep-learning methodology for conditional de novo design of α-helix structures.Bioinformatics (Oxford, England) · 2023Article
- Improving antibody optimization ability of generative adversarial network through large language model.Computational and structural biotechnology journal · 2023Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationProtein design has become increasingly important for medical and biotechnological applications. Because of the complex mechanisms underlying protein formation, the creation of a novel protein requires tedious and time-consuming computational or experimental protocols. At the same time, machine learning has enabled the solving of complex problems by leveraging large amounts of available data, more recently with great improvements on the domain of generative modeling. Yet, generative models have mainly been applied to specific sub-problems of protein design.
resultsHere, we approach the problem of general-purpose protein design conditioned on functional labels of the hierarchical Gene Ontology. Since a canonical way to evaluate generative models in this domain is missing, we devise an evaluation scheme of several biologically and statistically inspired metrics. We then develop the conditional generative adversarial network ProteoGAN and show that it outperforms several classic and more recent deep-learning baselines for protein sequence generation. We further give insights into the model by analyzing hyperparameters and ablation baselines. Lastly, we hypothesize that a functionally conditional model could generate proteins with novel functions by combining labels and provide first steps into this direction of research. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are available on GitHub at https://github.com/timkucera/proteogan, and can be accessed with doi:10.5281/zenodo.6591379. SUPPLEMENTARY INFORMATION: Supplemental data are available at Bioinformatics online.
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