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Updated: Jun 16, 2026
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Protein Target Prediction and Validation of Small Molecule Compound
Published on: February 23, 2024
Predicting Protein Function in the AI and Big Data Era
Riccardo Percudani1, Carlo De Rito1
1Department of Chemistry, Life Sciences and Environmental Sustainability, University of Parma, 43124 Parma, Italy.
Biochemistry
|May 17, 2025
Summary
Deep learning and AI are revolutionizing protein function prediction. These advanced methods enrich protein databases with 3D structures, improving our understanding of molecular life.
Area of Science:
- Proteomics and Bioinformatics
- Computational Biology
- Molecular Biology
Background:
- Traditional methods for determining protein function from genomic sequences are largely outdated.
- Recent advancements in big data analytics have spurred progress in functional genomics.
- The integration of artificial intelligence (AI) and deep learning (DL) is transforming biological research.
Purpose of the Study:
- To highlight the impact of AI and DL on predicting protein functions.
- To emphasize the enrichment of protein databases with structural and functional information.
- To discuss the potential of these methods in advancing molecular-level understanding of life.
Main Methods:
- Utilizing deep learning algorithms for analyzing genomic and proteomic data.
- Employing AI-based approaches to predict protein structures and functions.
- Integrating large-scale biological datasets to train predictive models.
Main Results:
- AI and DL methods are enhancing protein databases with crucial three-dimensional structural information.
- These advanced techniques offer the potential to accurately predict biochemical properties and biomolecular interactions.
- A significant increase in functionally annotated proteins is anticipated.
Conclusions:
- AI and deep learning represent a paradigm shift in understanding protein function.
- The enhanced functional insights will deepen our comprehension of biological processes at the molecular level.
- This progress promises to accelerate discoveries in molecular biology and related fields.
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