Role of Artificial Intelligence in bioinformatics: Revolutionizing molecular docking and DNA tokenization

Area of Science:

• Bioinformatics
• Computational Biology
• Artificial Intelligence

Background:

• Traditional bioinformatics methods struggle with complex biological datasets.
• Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for biosciences.
• AI models automate tasks, enhance data accuracy, and uncover hidden patterns.

Purpose of the Study:

• To review the significant contributions of AI and ML in bioinformatics.
• To focus on AI applications in molecular docking and DNA tokenization-based genomic analysis.
• To discuss current challenges and future directions for AI in bioinformatics.

Main Methods:

• Review of AI and ML advancements in bioinformatics.
• Focus on deep learning, transformer architectures, diffusion models, and foundation models.
• Analysis of AI-driven tools for molecular docking (e.g., DiffDock, AlphaFold) and genomic analysis (e.g., DNABERT, Enformer).

Main Results:

• AI and ML have significantly improved molecular docking, protein structure prediction, and virtual screening.
• Transformer-based models have revolutionized DNA tokenization and long-range genomic sequence analysis.
• AI enhances prediction accuracy and computational efficiency in various bioinformatics tasks.

Conclusions:

• AI and ML are pivotal in advancing bioinformatics, particularly in molecular docking and genomic analysis.
• Future potential lies in hybrid and biologically informed AI frameworks for next-generation bioinformatics.
• Addressing current challenges will further unlock the potential of AI in biological data interpretation.