ReviewMethods in molecular biology (Clifton, N.J.)2025
Beyond AlphaFold2: The Impact of AI for the Further Improvement of Protein Structure Prediction.
Review in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 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.
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Who cites it
21 citing papers in PubMed.
- The transformative impact of AI-enabled AlphaFold 3: evolution, current status, and future prospects in structural biology.Frontiers in artificial intelligence · 2026Review
- Tree-Based Methods to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Neural Networks to Calculate CDK2 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- CDK7 as a Target for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Machine Learning to Predict CDK4 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molegro Virtual Docker for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Gradient Descent to Predict Enzyme Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Hands-On Docking with Molegro Virtual Docker.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Calculating Enzyme Inhibition with Random Forests.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molegro Data Modeller for Machine Learning.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Extremely Randomized Trees to Determine Binding Affinity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Decision Tree for Prediction of Binding Affinity.Methods in molecular biology (Clifton, N.J.) · 2026Article
- AlphaFold for Docking Screens.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Neural Networks with Molegro Data Modeller.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Targeting CDK9 with Molegro Virtual Docker.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Differential Evolution for Docking Simulations.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Molegro Data Modeller to Estimate CDK6 Inhibition.Methods in molecular biology (Clifton, N.J.) · 2026Article
- New Sights into Bioinformatics of Gene Regulations and Structure.International journal of molecular sciences · 2025Article
- Structural Biology in the AlphaFold Era: How Far Is Artificial Intelligence from Deciphering the Protein Folding Code?Biomolecules · 2025Review
- Incorporating mRNA therapeutics into biological treatments of hematologic malignancies.Frontiers in immunology · 2025Review
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Protein structure prediction is fundamental to molecular biology and has numerous applications in areas such as drug discovery and protein engineering. Machine learning techniques have greatly advanced protein 3D modeling in recent years, particularly with the development of AlphaFold2 (AF2), which can analyze sequences of amino acids and predict 3D structures with near experimental accuracy. Since the release of AF2, numerous studies have been conducted, either using AF2 directly for large-scale modeling or building upon the software for other use cases. Many reviews have been published discussing the impact of AF2 in the field of protein bioinformatics, particularly in relation to neural networks, which have highlighted what AF2 can and cannot do. It is evident that AF2 and similar approaches are open to further development and several new approaches have emerged, in addition to older refinement approaches, for improving the quality of predictions. Here we provide a brief overview, aimed at the general biologist, of how machine learning techniques have been used for improvement of 3D models of proteins following AF2, and we highlight the impacts of these approaches. In the most recent experiment on the Critical Assessment of Techniques for Protein Structure Prediction (CASP15), the most successful groups all developed their own tools for protein structure modeling that were based at least in some part on AF2. This improvement involved employing techniques such as generative modeling, changing parameters such as dropout to generate more AF2 structures, and data-driven approaches including using alternative templates and MSAs.
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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.