ReviewComputational and structural biotechnology journal2023
From sequence to function through structure: Deep learning for protein design.
Review in Computational and structural biotechnology journal, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 43 papers.
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Who cites it
43 citing papers in PubMed.
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Improving expression of Photobacterium sp. JT-ISH-224 α2,6-sialyltransferase using ProteinMPNN and machine learning guided combinatory mutagenesis.Biotechnology letters · 2026Article
- Scaffold-Lab: Critical evaluation and ranking of protein backbone generation methods in a unified framework.PLoS computational biology · 2026Article
- Review
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- Automated and modular protein binder design with BinderFlow.PLoS computational biology · 2025Article
- Article
- Accessible, uniform protein property prediction with a scikit-learn based toolset AIDE.Bioinformatics (Oxford, England) · 2025Article
- Rational protein engineering using an omni-directional multipoint mutagenesis generation pipeline.iScience · 2025Article
- Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.Proteins · 2025Article
- Atomic context-conditioned protein sequence design using LigandMPNN.Nature methods · 2025Article
- Genetic engineering of RuBisCO by multiplex CRISPR editing small subunits in rice.Plant biotechnology journal · 2025Article
- AI Prediction of Structural Stability of Nanoproteins Based on Structures and Residue Properties by Mean Pooled Dual Graph Convolutional Network.Interdisciplinary sciences, computational life sciences · 2025Article
- Computational scoring and experimental evaluation of enzymes generated by neural networks.Nature biotechnology · 2025Article
- Structure-based computational design of antibody mimetics: challenges and perspectives.FEBS open bio · 2025Review
- Trends of Artificial Intelligence (AI) Use in Drug Targets, Discovery and Development: Current Status and Future Perspectives.Current drug targets · 2025Review
- Use of AI-methods over MD simulations in the sampling of conformational ensembles in IDPs.Frontiers in molecular biosciences · 2025Review
- Article
- Bilingual language model for protein sequence and structure.NAR genomics and bioinformatics · 2024Article
- Machine learning for predicting Plasmodium liver stage development in vitro using microscopy imaging.Computational and structural biotechnology journal · 2024Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The process of designing biomolecules, in particular proteins, is witnessing a rapid change in available tooling and approaches, moving from design through physicochemical force fields, to producing plausible, complex sequences fast via end-to-end differentiable statistical models. To achieve conditional and controllable protein design, researchers at the interface of artificial intelligence and biology leverage advances in natural language processing (NLP) and computer vision techniques, coupled with advances in computing hardware to learn patterns from growing biological databases, curated annotations thereof, or both. Once learned, these patterns can be used to provide novel insights into mechanistic biology and the design of biomolecules. However, navigating and understanding the practical applications for the many recent protein design tools is complex. To facilitate this, we 1) document recent advances in deep learning (DL) assisted protein design from the last three years, 2) present a practical pipeline that allows to go from
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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.