ReviewInternational journal of molecular sciences2021
Recent Applications of Deep Learning Methods on Evolution- and Contact-Based Protein Structure Prediction.
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 13 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 25 citations in OpenAlex.
- From Algorithms to Assets: A Comprehensive Review of AI's Role in Preclinical Drug Discovery and the Hurdles to Clinical Translation.Pharmaceuticals (Basel, Switzerland) · 2026Review
- Biological activity ofHelminthologia · 2026Article
- Calculating Enzyme Inhibition with Random Forests.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Review
- Protein-DNA interaction in tight-binding paradigm.Scientific reports · 2025Article
- Protein subcellular localization prediction tools.Computational and structural biotechnology journal · 2024Review
- Machine-learning-guided Directed Evolution for AAV Capsid Engineering.Current pharmaceutical design · 2024Review
- Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design).Methods in molecular biology (Clifton, N.J.) · 2024Article
- Molecular Docking and Molecular Dynamics Simulations in Related toTropical medicine and infectious disease · 2023Review
- Critical Assessment of Methods for Predicting the 3D Structure of Proteins and Protein Complexes.Annual review of biophysics · 2023Review
- Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).Biochemical Society transactions · 2022Article
- Deep learning in prediction of intrinsic disorder in proteins.Computational and structural biotechnology journal · 2022Review
- Exploiting Structural Modelling Tools to Explore Host-Translocated Effector Proteins.International journal of molecular sciences · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
Abstract
The new advances in deep learning methods have influenced many aspects of scientific research, including the study of the protein system. The prediction of proteins' 3D structural components is now heavily dependent on machine learning techniques that interpret how protein sequences and their homology govern the inter-residue contacts and structural organization. Especially, methods employing deep neural networks have had a significant impact on recent CASP13 and CASP14 competition. Here, we explore the recent applications of deep learning methods in the protein structure prediction area. We also look at the potential opportunities for deep learning methods to identify unknown protein structures and functions to be discovered and help guide drug-target interactions. Although significant problems still need to be addressed, we expect these techniques in the near future to play crucial roles in protein structural bioinformatics as well as in drug discovery.
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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.