ReviewInternational journal of molecular sciences2021
Protein Design with Deep Learning.
Review in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
24 citing papers in PubMed.
- A Generative Neuro-Symbolic AI for Protein Sequence Design.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Topology-Aware Generation and Activity-Based Filtering: A Computational-Experimental Framework for Data-Scarce Quaternary Ammonium Compound Discovery.Journal of chemical information and modeling · 2026Article
- Review
- IDP-Bert: Predicting Properties of Intrinsically Disordered Proteins Using Large Language Models.The journal of physical chemistry. B · 2024Article
- Structure-based protein and small molecule generation using EGNN and diffusion models: A comprehensive review.Computational and structural biotechnology journal · 2024Review
- Deep learning for discriminating non-trivial conformational changes in molecular dynamics simulations of SARS-CoV-2 spike-ACE2.Scientific reports · 2024Article
- SPDesign: protein sequence designer based on structural sequence profile using ultrafast shape recognition.Briefings in bioinformatics · 2024Article
- Graphormer supervised de novo protein design method and function validation.Briefings in bioinformatics · 2024Article
- Network of epistatic interactions in an enzyme active site revealed by large-scale deep mutational scanning.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Leveraging Artificial Intelligence to Expedite Antibody Design and Enhance Antibody-Antigen Interactions.Bioengineering (Basel, Switzerland) · 2024Review
- ProteinMPNN Recovers Complex Sequence Properties of Transmembrane β-barrels.bioRxiv : the preprint server for biology · 2024Article
- Machine-learning-guided Directed Evolution for AAV Capsid Engineering.Current pharmaceutical design · 2024Review
- Intelligent Protein Design and Molecular Characterization Techniques: A Comprehensive Review.Molecules (Basel, Switzerland) · 2023Review
- Homologous Pairs of Low and High Temperature Originating Proteins Spanning the Known Prokaryotic Universe.Scientific data · 2023Article
- A probabilistic view of protein stability, conformational specificity, and design.Scientific reports · 2023Article
- The Versatile Biocatalyst of Cytochrome P450 CYP102A1: Structure, Function, and Engineering.Molecules (Basel, Switzerland) · 2023Review
- Progress of the "Molecular Informatics" Section in 2022.International journal of molecular sciences · 2023Article
- Exploiting conformational dynamics to modulate the function of designed proteins.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Algorithmic Graph Theory, Reinforcement Learning and Game Theory in MD Simulations: From 3D Structures to Topological 2D-Molecular Graphs (2D-MolGraphs) and Vice Versa.Molecules (Basel, Switzerland) · 2023Article
- Neural network-derived Potts models for structure-based protein design using backbone atomic coordinates and tertiary motifs.Protein science : a publication of the Protein Society · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Computational Protein Design (CPD) has produced impressive results for engineering new proteins, resulting in a wide variety of applications. In the past few years, various efforts have aimed at replacing or improving existing design methods using Deep Learning technology to leverage the amount of publicly available protein data. Deep Learning (DL) is a very powerful tool to extract patterns from raw data, provided that data are formatted as mathematical objects and the architecture processing them is well suited to the targeted problem. In the case of protein data, specific representations are needed for both the amino acid sequence and the protein structure in order to capture respectively 1D and 3D information. As no consensus has been reached about the most suitable representations, this review describes the representations used so far, discusses their strengths and weaknesses, and details their associated DL architecture for design and related tasks.
Indexed as
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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.