ArticleProteins2020
ProDCoNN: Protein design using a convolutional neural network.
Article in Proteins, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
34 citing papers in PubMed.
- ProtSeqGen: a novel deep learning model for protein sequence design.BMC bioinformatics · 2026Article
- Enhancing CAR-T cell activity prediction via fine-tuning protein language models with generated CAR sequences.BMC bioinformatics · 2026Article
- Protein Design Enters the Artificial Intelligence Era: Foundations, Tools, and Emerging Paradigms.Computational and structural biotechnology journal · 2026Review
- PROPERMAB: an integrative framework formAbs · 2025Article
- GeoEvoBuilder: A deep learning framework for efficient functional and thermostable protein design.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Enhancing Functional Protein Design Using Heuristic Optimization and Deep Learning for Anti-Inflammatory and Gene Therapy Applications.Proteins · 2025Article
- DivPro: diverse protein sequence design with direct structure recovery guidance.Bioinformatics (Oxford, England) · 2025Article
- ScFold: a GNN-based model for efficient inverse folding of short-chain proteins via spatial reduction.Briefings in bioinformatics · 2025Article
- Protein engineering in the deep learning era.mLife · 2024Review
- PepCARES: A Comprehensive Advanced Refinement and Evaluation System for Peptide Design and Affinity Screening.ACS omega · 2024Article
- DIProT: A deep learning based interactive toolkit for efficient and effective Protein design.Synthetic and systems biotechnology · 2024Article
- Graphormer supervised de novo protein design method and function validation.Briefings in bioinformatics · 2024Article
- SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.Briefings in bioinformatics · 2024Article
- TIMED-Design: flexible and accessible protein sequence design with convolutional neural networks.Protein engineering, design & selection : PEDS · 2024Article
- SPIN-CGNN: Improved fixed backbone protein design with contact map-based graph construction and contact graph neural network.PLoS computational biology · 2023Article
- ProRefiner: an entropy-based refining strategy for inverse protein folding with global graph attention.Nature communications · 2023Article
- SeqPredNN: a neural network that generates protein sequences that fold into specified tertiary structures.BMC bioinformatics · 2023Article
- Review
- Accurate and efficient protein sequence design through learning concise local environment of residues.Bioinformatics (Oxford, England) · 2023Article
- Toward the appropriate interpretation of Alphafold2.Frontiers in artificial intelligence · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Designing protein sequences that fold to a given three-dimensional (3D) structure has long been a challenging problem in computational structural biology with significant theoretical and practical implications. In this study, we first formulated this problem as predicting the residue type given the 3D structural environment around the C
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What OpenQuestion holds
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.