Generative Artificial Intelligence in Bioinformatics: A Systematic Review of Models, Applications, and Methodological Advances

2026-07-25 · Source: cs.CL updates on arXiv.org · Field: Science & Research — Artificial Intelligence & Machine Learning, Life Sciences & Biology, Research Methodology & Innovation · Depth: Expert, extended

Summary

This systematic review analyzes Generative Artificial Intelligence (GenAI) models, applications, and methodological advances across bioinformatics, including genomics, proteomics, structural biology, and drug discovery. The review, structured around six research questions, highlights GenAI's transformative impact, demonstrating superior performance over traditional methods in diverse applications like sequence analysis and molecular design. Specialized architectures such as DNABERT and ESM-2 consistently outperform general-purpose models, an advantage attributed to targeted pretraining and context-aware strategies. Key improvements include protein structure prediction (e.g., ESMFold is 60 times faster than AlphaFold2), functional prediction, and de novo generation of proteins, genomes, and synthetic multi-omics data. The study also identifies limitations such as scalability, data bias, interpretability, and high computational costs, proposing future directions focused on modular, efficient, and biologically grounded models, supported by diverse molecular, cellular, and textual datasets.

Key takeaway

For Research Scientists and ML Engineers integrating GenAI into biological research, prioritize domain-specific models and multimodal data fusion over general-purpose LLMs for superior accuracy in tasks like protein design or multi-omics integration. Focus on parameter-efficient fine-tuning and modular frameworks to manage computational costs and enhance interpretability. Be mindful of data biases and ensure robust biological validation to build trustworthy, scalable solutions.

Key insights

Generative AI, particularly specialized models, is transforming bioinformatics by enabling de novo design and superior analytical performance.

Principles

• Domain-specific models excel over general LLMs.
• High-quality embeddings drive downstream performance.
• Multimodal data integration boosts accuracy.

In practice

• Design novel proteins with specific functions.
• Accelerate protein structure prediction 60x.
• Generate synthetic multi-omics data.

Topics

• Generative AI
• Bioinformatics
• Protein Language Models
• Multi-omics Integration
• Drug Discovery
• Protein Structure Prediction

Best for: AI Scientist, Research Scientist, Machine Learning Engineer

Related on AIssential

• Artificial intelligence and biology: AI’s potential for launching a novel era for health and medicine — AI is transforming biology by enabling large-scale analysis of complex interactions, moving towards causal understanding.
• M$^3$-Gen: Interpretable Multimodal Generation of Gene Expression Profiles Using Clinical and Imaging Data — M$^3$-Gen generates interpretable gene expression profiles from clinical and imaging data using a GAN and contrastive learning.
• Data-driven surrogates of rational design enable antimicrobial peptide optimization — Generative AI and data-driven surrogates enable rapid optimization of antimicrobial peptides against drug resistance.
• From discovery to design: in conversation with Ali Madani (Profluent) — AI-driven language models can design novel, functional proteins from scratch, accelerating biological evolution for therapeutic applications.
• Radical Numerics launches with $50M to build general biological intelligence — Multimodal AI models integrating diverse biological data can advance human health through diagnostics and drug discovery while simultaneously bolstering biosecurity.
• DrugGen 2: A disease-aware language model for enhancing drug discovery — DrugGen-2 is a disease-aware generative AI model that significantly improves de novo drug design by integrating disease ontology.

See Counsel's argued verdicts on the open AI decisions leaders are weighing →

Open in AIssential →

Editorial summary, takeaway, and curation by AIssential. Original article published by cs.CL updates on arXiv.org.