ReviewTrends in biochemical sciences2023
Novel machine learning approaches revolutionize protein knowledge.
Review in Trends in biochemical sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
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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.
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Who cites it
41 citing papers in PubMed.
- GATSBI: improving context-aware protein embeddings through biologically motivated data splits.Bioinformatics (Oxford, England) · 2026Article
- DeepPath: overcoming data scarcity for protein transition pathway prediction using physics-based deep learning.Chemical science · 2026Article
- Optimization of the Quantum-Si Platinum Single-Molecule Protein Sequencing Platform Toward Improved Complex-Matrix Protein Identification.ACS omega · 2026Article
- Protein Large Language Models Can PredictACS omega · 2026Article
- GATSBI: Improving context-aware protein embeddings through biologically motivated data splits.bioRxiv : the preprint server for biology · 2026Article
- PlasticEnz: An integrated database and screening tool combining homology and machine learning to identify plastic-degrading enzymes in meta-omics datasets.PLoS computational biology · 2026Article
- The Role of AI-Driven De Novo Protein Design in the Exploration of the Protein Functional Universe.Biology · 2025Review
- AlphaFold 3: an unprecedent opportunity for fundamental research and drug development.Precision clinical medicine · 2025Review
- Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics.Briefings in bioinformatics · 2025Review
- Predicting Protein Function in the AI and Big Data Era.Biochemistry · 2025Review
- From data to discovery: leveraging big data in plant natural products biosynthesis research.The Plant journal : for cell and molecular biology · 2025Review
- Cutting-edge deep-learning based tools for metagenomic research.National science review · 2025Review
- Unicore Enables Scalable and Accurate Phylogenetic Reconstruction with Structural Core Genes.Genome biology and evolution · 2025Article
- Design of overlapping genes using deep generative models of protein sequences.bioRxiv : the preprint server for biology · 2025Article
- Article
- A Survey of Biological Function Prediction Methods with Focus on Natural Language Processing (NLP) and Large Language Models (LLM).Methods in molecular biology (Clifton, N.J.) · 2025Article
- Challenges in bridging the gap between protein structure prediction and functional interpretation.Proteins · 2025Article
- Reliability of plastid and mitochondrial localisation prediction declines rapidly with the evolutionary distance to the training set increasing.PLoS computational biology · 2024Article
- An explainable language model for antibody specificity prediction using curated influenza hemagglutinin antibodies.Immunity · 2024Article
- Complex state transitions of the bacterial cell division protein FtsZ.Molecular biology of the cell · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
Breakthrough methods in machine learning (ML), protein structure prediction, and novel ultrafast structural aligners are revolutionizing structural biology. Obtaining accurate models of proteins and annotating their functions on a large scale is no longer limited by time and resources. The most recent method to be top ranked by the Critical Assessment of Structure Prediction (CASP) assessment, AlphaFold 2 (AF2), is capable of building structural models with an accuracy comparable to that of experimental structures. Annotations of 3D models are keeping pace with the deposition of the structures due to advancements in protein language models (pLMs) and structural aligners that help validate these transferred annotations. In this review we describe how recent developments in ML for protein science are making large-scale structural bioinformatics available to the general scientific community.
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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.