AI-Driven Deep Learning Techniques in Protein Structure Prediction
importance 8/10
partially solved
Protein Structure and Dynamics protein science stage: research
Problem
Predicting protein 3D structure from sequence is a long-standing computational challenge important for understanding function.
Why it matters
Protein structures underpin drug design and understanding of disease and biological function.
What the AI did
This review surveys AI frameworks such as AlphaFold, RoseTTAFold and ProteinBERT, and their integration into established tools, comparing performance via CASP14/CASP15 rankings.
Results
not stated in source
Impact
Documents AI's central role in advancing protein structure prediction, benchmarked via CASP rankings.
Record
| Venue | International Journal of Molecular Sciences |
|---|---|
| Published | 2024 |
| Work type | article |
| Authors | Lingtao Chen, Qiaomu Li, Kazi Fahim Ahmad Nasif, Ying Xie, Bobin Deng, Shuteng Niu, Seyedamin Pouriyeh, Zhiyu Dai et al. |
| Citations | 136 (OpenAlex, at import time) |
| References | 130 |
| Model or method | AlphaFold (1-3), RoseTTAFold, ProteinBERT |
| Organization | not stated in source |
| Primary source | https://doi.org/10.3390/ijms25158426 |
| Open access | gold |
| Keywords | protein structure prediction, AlphaFold, deep learning, CASP, review |
Importance rating
8
major — millions of lives, an industry or a field
Protein structure prediction is a foundational scientific field underpinning understanding of function and behavior; abstract gives no quantified scale of beneficiaries.