GenAI

Generative AI in Bioinformatics: What a New Systematic Review Finds

Kari Jaaskelainen

06 Nov 2025 — 1 min read

GenAI is reshaping genomics, proteomics, transcriptomics, structural biology, and drug discovery. This systematic review asks six big questions to map where GenAI helps most—and what’s next.

Where it works: Sequence analysis, molecular design, and integrative modeling often beat traditional methods via pattern discovery and realistic output generation.
Best models: Domain-specialized architectures with targeted pretraining and context-aware inputs outperform general-purpose models.
Big wins: Better molecular analysis and data integration improve accuracy and cut errors in complex pipelines.
Validated gains: Stronger structural modeling, functional prediction, and synthetic data generation on standard benchmarks.
Limits: Scalability and dataset bias still curb generalization—calling for tougher evaluations and biologically grounded modeling.
Data fuel: Molecular sets (UniProtKB, ProteinNet12), cellular atlases (CELLxGENE, GTEx), and text corpora (PubMedQA, OMIM) power training and transfer.

Takeaway: GenAI isn’t a silver bullet, but specialized, well-evaluated models are already delivering practical gains across the bioinformatics stack.

Paper: http://arxiv.org/abs/2511.03354v1

Paper: http://arxiv.org/abs/2511.03354v1

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GenAI Bioinformatics Genomics Proteomics StructuralBiology DrugDiscovery AI SystematicReview MachineLearning

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